Coherent change detection(CCD) is an effective method to detect subtle scene changes that occur between temporal synthetic aperture radar(SAR) observations. Most coherence estimators are obtained from a Hermitian prod...Coherent change detection(CCD) is an effective method to detect subtle scene changes that occur between temporal synthetic aperture radar(SAR) observations. Most coherence estimators are obtained from a Hermitian product based on local statistics. Increasing the number of samples in the local window can improve the estimation bias, but cause the loss of the estimated images spatial resolution. The limitations of these estimators lead to unclear contour of the disturbed region, and even the omission of fine change targets. In this paper, a CCD approach is proposed to detect fine scene changes from multi-temporal and multi-angle SAR image pairs. Multi-angle CCD estimator can improve the contrast between the change target and the background clutter by jointly accumulating singleangle alternative estimator results without further loss of image resolution. The sensitivity of detection performance to image quantity and angle interval is analyzed. Theoretical analysis and experimental results verify the performance of the proposed algorithm.展开更多
Recently,ground coverings change detection(CD)driven by bitemporal hyperspectral images(HSIs)has become a hot topic in the remote sensing community.There are two challenges in the HSI‐CD task:(1)attribute feature rep...Recently,ground coverings change detection(CD)driven by bitemporal hyperspectral images(HSIs)has become a hot topic in the remote sensing community.There are two challenges in the HSI‐CD task:(1)attribute feature representation of pixel pairs and(2)feature extraction of attribute patterns of pixel pairs.To solve the above problems,a novel spectral‐spatial sequence characteristics‐based convolutional transformer(S3C‐CT)method is proposed for the HSI‐CD task.In the designed method,firstly,an eigenvalue extrema‐based band selection strategy is introduced to pick up spectral information with salient attribute patterns.Then,a 3D tensor with spectral‐spatial sequence characteristics is proposed to represent the attribute features of pixel pairs in the bitemporal HSIs.Next,a fusion framework of the convolutional neural network(CNN)and Transformer encoder(TE)is designed to extract high‐order sequence semantic features,taking into account both local context information and global sequence dependencies.Specifically,a spatial‐spectral attention mechanism is employed to prevent information reduction and enhance dimensional interactivity between the CNN and TE.Finally,the binary change map is determined according to the fully‐connected layer.Experimental results on real HSI datasets indicated that the proposed S3C‐CT method outperforms other well‐known and state‐of‐the‐art detection approaches in terms of detection performance.展开更多
Land use/land cover (LULC) mapping and change detection are fundamental aspects of remote sensing data application. Therefore, selecting an appropriate classifier approach is crucial for accurate classification and ch...Land use/land cover (LULC) mapping and change detection are fundamental aspects of remote sensing data application. Therefore, selecting an appropriate classifier approach is crucial for accurate classification and change assessment. In the first part of this study, the performance of machine learning classification algorithms was compared using Landsat 9 image (2023) of the Manouba government (Tunisia). Three different classification methods were applied: Maximum Likelihood Classification (MLC), Support Vector Machine (SVM), and Random Trees (RT). The classification aimed to identify five land use classes: urban area, vegetation, bare area, water and forest. A qualitative assessment was conducted using Overall Accuracy (OA) and the Kappa coefficient (K), derived from a confusion matrix. The results of the land cover classification demonstrated a high level of accuracy. The SVM method exhibited the best performance, with an overall accuracy of 93% and a kappa accuracy of 0.9. The ML method is the second-best classifier with an overall accuracy of 92% and a kappa accuracy of 0.88. The Random Trees method yielded the lowest accuracy among the three approaches, with an overall accuracy of 91% and a kappa accuracy of 0.87. The second part of the study focused on analyzing LULC changes in the study area. Based on the classification results, the SVM method was chosen to classify the Landsat 7 image acquired in 2000. LULC changes from 2000 to 2023 were investigated using change detection comparison. The findings indicate that over the last 23 years, vegetation land and urban areas in the study area have experienced significant increases of 31.94% and 5.47%, respectively. This study contributed to a better understanding of the classification process and dynamic LULC changes in the Manouba region. It provided valuable insights for decision-makers in planning land conservation and management.展开更多
Objectives:When detecting changes in synthetic aperture radar(SAR)images,the quality of the difference map has an important impact on the detection results,and the speckle noise in the image interferes with the extrac...Objectives:When detecting changes in synthetic aperture radar(SAR)images,the quality of the difference map has an important impact on the detection results,and the speckle noise in the image interferes with the extraction of change information.In order to improve the detection accuracy of SAR image change detection and improve the quality of the difference map,this paper proposes a method that combines the popular deep neural network with the clustering algorithm.Methods:Firstly,the SAR image with speckle noise was constructed,and the FFDNet architecture was used to retrain the SAR image,and the network parameters with better effect on speckle noise suppression were obtained.Then the log ratio operator is generated by using the reconstructed image output from the network.Finally,K-means and FCM clustering algorithms are used to analyze the difference images,and the binary map of change detection results is generated.Results:The experimental results have high detection accuracy on Bern and Sulzberger’s real data,which proves the effectiveness of the method.展开更多
Semantic change detection is extension of change detection task in which it is not only used to identify the changed regions but also to analyze the land area semantic(labels/categories)details before and after the ti...Semantic change detection is extension of change detection task in which it is not only used to identify the changed regions but also to analyze the land area semantic(labels/categories)details before and after the timelines are analyzed.Periodical land change analysis is used for many real time applications for valuation purposes.Majority of the research works are focused on Convolutional Neural Networks(CNN)which tries to analyze changes alone.Semantic information of changes appears to be missing,there by absence of communication between the different semantic timelines and changes detected over the region happens.To overcome this limitation,a CNN network is proposed incorporating the Resnet-34 pre-trained model on Fully Convolutional Network(FCN)blocks for exploring the temporal data of satellite images in different timelines and change map between these two timelines are analyzed.Further this model achieves better results by analyzing the semantic information between the timelines and based on localized information collected from skip connections which help in generating a better change map with the categories that might have changed over a land area across timelines.Proposed model effectively examines the semantic changes such as from-to changes on land over time period.The experimental results on SECOND(Semantic Change detectiON Dataset)indicates that the proposed model yields notable improvement in performance when it is compared with the existing approaches and this also improves the semantic segmentation task on images over different timelines and the changed areas of land area across timelines.展开更多
In this paper,an unsupervised change detection technique for remote sensing images acquired on the same geographical area but at different time instances is proposed by conducting Covariance Intersection(CI) to perfor...In this paper,an unsupervised change detection technique for remote sensing images acquired on the same geographical area but at different time instances is proposed by conducting Covariance Intersection(CI) to perform unsupervised fusion of the final fuzzy partition matrices from the Fuzzy C-Means(FCM) clustering for the feature space by applying compressed sampling to the given remote sensing images.The proposed approach exploits a CI-based data fusion of the membership function matrices,which are obtained by taking the Fuzzy C-Means(FCM) clustering of the frequency-domain feature vectors and spatial-domain feature vectors,aimed at enhancing the unsupervised change detection performance.Compressed sampling is performed to realize the image local feature sampling,which is a signal acquisition framework based on the revelation that a small collection of linear projections of a sparse signal contains enough information for stable recovery.The experimental results demonstrate that the proposed algorithm has a good change detection results and also performs quite well on denoising purpose.展开更多
Change detection(CD)is becoming indispensable for unmanned aerial vehicles(UAVs),especially in the domain of water landing,rescue and search.However,even the most advanced models require large amounts of data for mode...Change detection(CD)is becoming indispensable for unmanned aerial vehicles(UAVs),especially in the domain of water landing,rescue and search.However,even the most advanced models require large amounts of data for model training and testing.Therefore,sufficient labeled images with different imaging conditions are needed.Inspired by computer graphics,we present a cloning method to simulate inland-water scene and collect an auto-labeled simulated dataset.The simulated dataset consists of six challenges to test the effects of dynamic background,weather,and noise on change detection models.Then,we propose an image translation framework that translates simulated images to synthetic images.This framework uses shared parameters(encoder and generator)and 22×22 receptive fields(discriminator)to generate realistic synthetic images as model training sets.The experimental results indicate that:1)different imaging challenges affect the performance of change detection models;2)compared with simulated images,synthetic images can effectively improve the accuracy of supervised models.展开更多
With the rapid development of Unmanned Aerial Vehicle(UAV)technology,change detection methods based on UAV images have been extensively studied.However,the imaging of UAV sensors is susceptible to environmental interf...With the rapid development of Unmanned Aerial Vehicle(UAV)technology,change detection methods based on UAV images have been extensively studied.However,the imaging of UAV sensors is susceptible to environmental interference,which leads to great differences of same object between UAV images.Overcoming the discrepancy difference between UAV images is crucial to improving the accuracy of change detection.To address this issue,a novel unsupervised change detection method based on structural consistency and the Generalized Fuzzy Local Information C-means Clustering Model(GFLICM)was proposed in this study.Within this method,the establishment of a graph-based structural consistency measure allowed for the detection of change information by comparing structure similarity between UAV images.The local variation coefficient was introduced and a new fuzzy factor was reconstructed,after which the GFLICM algorithm was used to analyze difference images.Finally,change detection results were analyzed qualitatively and quantitatively.To measure the feasibility and robustness of the proposed method,experiments were conducted using two data sets from the cities of Yangzhou and Nanjing.The experimental results show that the proposed method can improve the overall accuracy of change detection and reduce the false alarm rate when compared with other state-of-the-art change detection methods.展开更多
People have an inherent tenacity to throng coastal regions in pursuit of better living conditions. As such the brisk dynamism of land use/land cover activities in a coastal region becomes obvious. The former keeps cha...People have an inherent tenacity to throng coastal regions in pursuit of better living conditions. As such the brisk dynamism of land use/land cover activities in a coastal region becomes obvious. The former keeps changing rapidly due to burgeoning population. A digital change detection analysis is performed with the help of Geographic Information System (GIS) on the Remote Sensing data spanning over last 20 years, complemented by in-situ data and ground truth information. This current research briefly endeavours to find out the nature of change happening in the major three coastal cities of Papua New Guinea (PNG), namely Alotau, capital of Milnebay province;Lae, capital of Morobe province and Port Moresby, capital of Papua New Guinea. Changes in land use and land cover that took place over 20 years have been recorded using Landsat 5 thematic mapper (TM) data of 1992 and Landsat 8 operational land imager (OLI) data. Land use and land cover maps of 1992, and 2013/14, and change detection matrix of 1992-2013/14 are derived. Results show an immensely sprawling urban landscape, evincing about five times growth during 1992 to 2014. At the same time “natural forests” dwindled by 444.96 hectares in Alotau, 6977.25 hectares in Lae and “mangrove” and “grass/shrub land” decreased by 127.78 and 4859.39 hectares respectively around Port Moresby. The above changes owe to ever increasing population pressure, land tenure shift, agriculture and industrial development.展开更多
Detection of structural changes from an opera- tional process is a major goal in machine condition moni- toring. Existing methods for this purpose are mainly based on retrospective analysis, resulting in a large detec...Detection of structural changes from an opera- tional process is a major goal in machine condition moni- toring. Existing methods for this purpose are mainly based on retrospective analysis, resulting in a large detection delay that limits their usages in real applications. This paper presents a new adaptive real-time change detection algorithm, an extension of the recent research by combin- ing with an incremental sliding-window strategy, to handle the multi-change detection in long-term monitoring of machine operations. In particular, in the framework, Hil- bert space embedding of distribution is used to map the original data into the Re-producing Kernel Hilbert Space (RK_HS) for change detection; then, a new adaptive threshold strategy can be developed when making change decision, in which a global factor (used to control the coarse-to-fine level of detection) is introduced to replace the fixed value of threshold. Through experiments on a range of real testing data which was collected from an experimental rotating machinery system, the excellent detection performances of the algorithm for engineering applications were demonstrated. Compared with state-of- the-art methods, the proposed algorithm can be more suitable for long-term machinery condition monitoring without any manual re-calibration, thus is promising in modern industries.展开更多
Continuous and accurate monitoring of earth surface changes over rugged terrain Himalayas is important to manage natural resources and mitigate natural hazards.Conventional techniques generally focus on per-pixel base...Continuous and accurate monitoring of earth surface changes over rugged terrain Himalayas is important to manage natural resources and mitigate natural hazards.Conventional techniques generally focus on per-pixel based processing and overlook the sub-pixel variations occurring especially in case of low or moderate resolution remotely sensed data.However,the existing subpixel-based change detection(SCD)models are less effective to detect the mixed pixel information at its complexity level especially over rugged terrain regions.To overcome such issues,a topographically controlled SCD model has been proposed which is an improved version of widely used per-pixel based change vector analysis(CVA)and hence,named as a subpixel-based change vector analysis(SCVA).This study has been conducted over a part of the Western Himalayas using the advanced wide-field sensor(AWiFS)and Landsat-8 datasets.To check the effectiveness of the proposed SCVA,the cross-validation of the results has been done with the existing neural network-based SCD(NN-SCD)and per-pixel based models such as fuzzybasedCVA(FCVA)andpost-classification comparison(PCC).The results have shown that SCVA offered robust performance(85.6%-86.4%)as comparedtoNN-SCD(81.6%-82.4%),PCC(79.2%-80.4%),and FCVA(81.2%-83.6%).We concluded that SCVA helps in reducing the detection of spurious pixels and improve the efficacy of generating change maps.This study is beneficial for the accurate monitoring of glacier retreat and snow cover variability over rugged terrain regions using moderate resolution remotely sensed datasets.展开更多
Constrained by complex imaging mechanism and extraordinary visual appearance,change detection with synthetic aperture radar(SAR)images has been a difficult research topic,especially in urban areas.Although existing st...Constrained by complex imaging mechanism and extraordinary visual appearance,change detection with synthetic aperture radar(SAR)images has been a difficult research topic,especially in urban areas.Although existing studies have extended from bi-temporal data pair to multi-temporal datasets to derive more plentiful information,there are still two problems to be solved in practical applications.First,change indicators constructed from incoherent feature only cannot characterize the change objects accurately.Second,the results of pixel-level methods are usually presented in the form of the noisy binary map,making the spatial change not intuitive and the temporal change of a single pixel meaningless.In this study,we propose an unsupervised man-made objects change detection framework using both coherent and incoherent features derived from multi-temporal SAR images.The coefficients of variation in timeseries incoherent features and the man-made object index(MOI)defined with coherent features are first combined to identify the initial change pixels.Afterwards,an improved spatiotemporal clustering algorithm is developed based on density-based spatial clustering of applications with noise(DBSCAN)and dynamic time warping(DTW),which can transform the initial results into noiseless object-level patches,and take the cluster center as a representative of the man-made object to determine the change pattern of each patch.An experiment with a stack of 10 TerraSAR-X images in Stripmap mode demonstrated that this method is effective in urban scenes and has the potential applicability to wide area change detection.展开更多
The study is aimed at analyzing the risk of Taita Hills region of harmful runoff and soil erosion by employing morphometric analysis and change detection in a GIS environment to prioritize the Taita Hills in Taita Tav...The study is aimed at analyzing the risk of Taita Hills region of harmful runoff and soil erosion by employing morphometric analysis and change detection in a GIS environment to prioritize the Taita Hills in Taita Taveta County. The objective of the study was to characterize and give hierarchy in which the region should be conserved. The methodology adopted hydrological modeling, morphometric computation, Weighted Sum Analysis (WSA) and change detection. Hydrological modeling was vital in delineating the sub-watersheds and stream network. Morphometric computation and WSA was applicable in coming up with parameters and weighting the parameters for each sub-watershed’s prioritization. Change detection is related to how human activity is important for conservation as the effect of land forms and dimensions are compounded. Twenty-one fourth order streamed sub-watersheds were generated and prioritized using morphometry and change detection. Every sub-watershed is given a hierarchy based on the calculated compound parameter from the WSA equation developed and shows the risk of runoff and soil erosion. The morphometric prioritization shows 47% of the watersheds are in the high and very highly susceptible areas and there are two sub-watersheds with the highest land cover change. As well six sub-watersheds are risky with both land cover change and morphometry.展开更多
Because of quick development of cities, the update of urban GIS data is very important. Change detection is the base of automatic or semi-automatic data update. One way of change detections in urban area is based on o...Because of quick development of cities, the update of urban GIS data is very important. Change detection is the base of automatic or semi-automatic data update. One way of change detections in urban area is based on old and new aerial images acquired in different durations. The corresponding theory and experiments are introduced and analyzed in this paper. The main procedure includes four stages. The new and old images have to be registered firstly. Then image matching, based on the maximum correlation coefficient, is performed between registered images after the low contrast areas have been removed. The regions with low matching quality are extracted as candidate changed areas. Thirdly, the Gaussian-Laplacian operator is used to detect edges in candidate changed areas on both the registered images, and the straight lines are detected by Hough transformation. Finally, the changed houses and roads can be detected on the basis of straight line matching in candidate changed areas between registered images.展开更多
Mapping rice cultivation is indispensable for monitoring food supply conditions in Bangladesh because of the economical importance of the crop for supporting ever increasing population in the country. In this paper, w...Mapping rice cultivation is indispensable for monitoring food supply conditions in Bangladesh because of the economical importance of the crop for supporting ever increasing population in the country. In this paper, we extract the rice paddy field using the MODIS satellite data for five districts of Pabna, Manikganj, Sherpur, Sylhet, and Gazipur, each of which is characterized with its own aspects in terms of rice cultivation. Land classification is implemented using the vegetation index information derived from the red (band 1) and near-infrared (band 2) bands of MODIS 8-day composite time series data for the two time periods of 2001-2003 and 2011-2013. Results of unsupervised classification indicate that the paddy area coverage increased about 4% and 1% in Gazipur and Sylhet, respectively. In Pabna, Manikganj, and Sherpur, on the other hand, paddy area decreased by 10%, 2% and 5%, respectively, whereas notable increase of 12%, 2% and 7% was found in homestead area coverage, which is becoming more and more important for better management of small-scale agroforestry. At the same time, in Sherpur and Sylhet, forest area increased by 1% and 2% over the same time period. As a validation of these results, the changes detected in Gazipur are compared with those previously derived from the analysis of Landsat data with higher spatial resolution of 30 m as compared with that of MODIS (250 m). Also, the seasonal rice cropping pattern is studied in these five districts for discriminating cultivated rice types. These changes suggest that as a whole, efforts are being made to increase the food production, though the influence of population pressure and economic growth is apparent in these regions.展开更多
The change of house often brings on the change of DSM in an area over different periods. If we can apply information of the height of houses to reinforce the house change detection, the reliability and efficiency of d...The change of house often brings on the change of DSM in an area over different periods. If we can apply information of the height of houses to reinforce the house change detection, the reliability and efficiency of detection methods will be improved greatly.From this viewpoint, a new approach taking advantage of both height data from an image pair and image data is proposed to detect the house change in urban area and is called 'data fusion technology'.展开更多
Land use/land cover (LULC) changes have become a central issue in current global change and sustainability research. Saudi Arabia has undergone significant change in land use and land cover since the government embark...Land use/land cover (LULC) changes have become a central issue in current global change and sustainability research. Saudi Arabia has undergone significant change in land use and land cover since the government embarked on a course of intense national development 30 years ago, as a result of huge national oil revenues. This study evaluates LULC change in Makkah and Al-Taif, Saudi Arabia from 1986 to 2013 using Landsat images. Maximum likelihood and object-oriented classification were used to develop LULC maps. The change detection was executed using post-classification comparison and GIS. The results indicated that urban areas have increased over the period by approximately 174% in Makkah and 113% in Al-Taif. Analysis of vegetation cover over the study area showed a variable distribution from year to year due to changing average precipitation in this environment. Object-based classification provided slightly greater accuracy than maximum likelihood classification. Information provided by satellite remote sensing can play an important role in quantifying and understanding the relationship between population growth and LULC changes, which can assist future planning and potential environmental impacts of expanding urban areas.展开更多
This paper presents a framework for road network change detection in order to update the Canadian National Topographic DataBase (NTDB). The methodology has been developed on the basis of road extraction from IRS\|pan ...This paper presents a framework for road network change detection in order to update the Canadian National Topographic DataBase (NTDB). The methodology has been developed on the basis of road extraction from IRS\|pan images (with a 5.8 m spatial resolution) by using a wavelet approach. The feature matching and conflation techniques are used to road change detection and updating. Elementary experiments have showed that the proposed framework could be used for developing an operational road database updating system.展开更多
The problems and difficulty of current change detection techniques are presented. Then, according to whether image registration is done before change detection algorithms, the authors classify the change detection int...The problems and difficulty of current change detection techniques are presented. Then, according to whether image registration is done before change detection algorithms, the authors classify the change detection into two categories:the change detection after image registration and the change detection simultaneous with image registration. For the former, four topics including the change detection between new image and old image, the change detection between new image and old map, the change detection between new image/old image and old map, and the change detection between new multi-source images and old map/image are introduced. For the latter, three categories, i.e. the change detection between old DEM, DOM and new non-rectification image, the change detection between old DLG, DRG and new non-rectification image, and the 3D change detection between old 4D products and new multi-overlapped photos, are discussed.展开更多
Due to inappropriate planning and management, accelerated urban growth and tremendous loss in land, especially cropland, have become a great challenge for sustainable urban development in China, especially in develope...Due to inappropriate planning and management, accelerated urban growth and tremendous loss in land, especially cropland, have become a great challenge for sustainable urban development in China, especially in developed urban area in the coastal regions; therefore, there is an urgent need to effectively detect and monitor the land use changes and provide accurate and timely information for planning and management. In this study a method combining principal component analysis (PCA) of multisensor satellite images from SPOT (systeme pour l'observation de la terre or earth observation satellite)-5 multispectral (XS) and Landsat-7 enhanced thematic mapper (ETM) panchromatic (PAN) data, and supervised classification was used to detect and analyze the dynamics of land use changes in the city proper of Hangzhou. The overall accuracy of the land use change detection was 90.67% and Kappa index was 0.89. The results indicated that there was a considerable land use change (10.03% of the total area) in the study area from 2001 to 2003, with three major types of land use conversions: from cropland into built-up land, construction site, and water area (fish pond). Changes from orchard land into built-up land were also detected. The method described in this study is feasible and useful for detecting rapid land use change in the urban area.展开更多
文摘Coherent change detection(CCD) is an effective method to detect subtle scene changes that occur between temporal synthetic aperture radar(SAR) observations. Most coherence estimators are obtained from a Hermitian product based on local statistics. Increasing the number of samples in the local window can improve the estimation bias, but cause the loss of the estimated images spatial resolution. The limitations of these estimators lead to unclear contour of the disturbed region, and even the omission of fine change targets. In this paper, a CCD approach is proposed to detect fine scene changes from multi-temporal and multi-angle SAR image pairs. Multi-angle CCD estimator can improve the contrast between the change target and the background clutter by jointly accumulating singleangle alternative estimator results without further loss of image resolution. The sensitivity of detection performance to image quantity and angle interval is analyzed. Theoretical analysis and experimental results verify the performance of the proposed algorithm.
基金supported in part by by the National Key R&D Program of China under Grant 2022YFB3903402in part by the National Natural Science Foundation of China under Grant 42222106in part by the National Natural Science Foundation of China under Grant 61976234 and 42201340。
文摘Recently,ground coverings change detection(CD)driven by bitemporal hyperspectral images(HSIs)has become a hot topic in the remote sensing community.There are two challenges in the HSI‐CD task:(1)attribute feature representation of pixel pairs and(2)feature extraction of attribute patterns of pixel pairs.To solve the above problems,a novel spectral‐spatial sequence characteristics‐based convolutional transformer(S3C‐CT)method is proposed for the HSI‐CD task.In the designed method,firstly,an eigenvalue extrema‐based band selection strategy is introduced to pick up spectral information with salient attribute patterns.Then,a 3D tensor with spectral‐spatial sequence characteristics is proposed to represent the attribute features of pixel pairs in the bitemporal HSIs.Next,a fusion framework of the convolutional neural network(CNN)and Transformer encoder(TE)is designed to extract high‐order sequence semantic features,taking into account both local context information and global sequence dependencies.Specifically,a spatial‐spectral attention mechanism is employed to prevent information reduction and enhance dimensional interactivity between the CNN and TE.Finally,the binary change map is determined according to the fully‐connected layer.Experimental results on real HSI datasets indicated that the proposed S3C‐CT method outperforms other well‐known and state‐of‐the‐art detection approaches in terms of detection performance.
文摘Land use/land cover (LULC) mapping and change detection are fundamental aspects of remote sensing data application. Therefore, selecting an appropriate classifier approach is crucial for accurate classification and change assessment. In the first part of this study, the performance of machine learning classification algorithms was compared using Landsat 9 image (2023) of the Manouba government (Tunisia). Three different classification methods were applied: Maximum Likelihood Classification (MLC), Support Vector Machine (SVM), and Random Trees (RT). The classification aimed to identify five land use classes: urban area, vegetation, bare area, water and forest. A qualitative assessment was conducted using Overall Accuracy (OA) and the Kappa coefficient (K), derived from a confusion matrix. The results of the land cover classification demonstrated a high level of accuracy. The SVM method exhibited the best performance, with an overall accuracy of 93% and a kappa accuracy of 0.9. The ML method is the second-best classifier with an overall accuracy of 92% and a kappa accuracy of 0.88. The Random Trees method yielded the lowest accuracy among the three approaches, with an overall accuracy of 91% and a kappa accuracy of 0.87. The second part of the study focused on analyzing LULC changes in the study area. Based on the classification results, the SVM method was chosen to classify the Landsat 7 image acquired in 2000. LULC changes from 2000 to 2023 were investigated using change detection comparison. The findings indicate that over the last 23 years, vegetation land and urban areas in the study area have experienced significant increases of 31.94% and 5.47%, respectively. This study contributed to a better understanding of the classification process and dynamic LULC changes in the Manouba region. It provided valuable insights for decision-makers in planning land conservation and management.
文摘Objectives:When detecting changes in synthetic aperture radar(SAR)images,the quality of the difference map has an important impact on the detection results,and the speckle noise in the image interferes with the extraction of change information.In order to improve the detection accuracy of SAR image change detection and improve the quality of the difference map,this paper proposes a method that combines the popular deep neural network with the clustering algorithm.Methods:Firstly,the SAR image with speckle noise was constructed,and the FFDNet architecture was used to retrain the SAR image,and the network parameters with better effect on speckle noise suppression were obtained.Then the log ratio operator is generated by using the reconstructed image output from the network.Finally,K-means and FCM clustering algorithms are used to analyze the difference images,and the binary map of change detection results is generated.Results:The experimental results have high detection accuracy on Bern and Sulzberger’s real data,which proves the effectiveness of the method.
文摘Semantic change detection is extension of change detection task in which it is not only used to identify the changed regions but also to analyze the land area semantic(labels/categories)details before and after the timelines are analyzed.Periodical land change analysis is used for many real time applications for valuation purposes.Majority of the research works are focused on Convolutional Neural Networks(CNN)which tries to analyze changes alone.Semantic information of changes appears to be missing,there by absence of communication between the different semantic timelines and changes detected over the region happens.To overcome this limitation,a CNN network is proposed incorporating the Resnet-34 pre-trained model on Fully Convolutional Network(FCN)blocks for exploring the temporal data of satellite images in different timelines and change map between these two timelines are analyzed.Further this model achieves better results by analyzing the semantic information between the timelines and based on localized information collected from skip connections which help in generating a better change map with the categories that might have changed over a land area across timelines.Proposed model effectively examines the semantic changes such as from-to changes on land over time period.The experimental results on SECOND(Semantic Change detectiON Dataset)indicates that the proposed model yields notable improvement in performance when it is compared with the existing approaches and this also improves the semantic segmentation task on images over different timelines and the changed areas of land area across timelines.
基金Supported by the National Natural Science Foundation of China(No.61071163)
文摘In this paper,an unsupervised change detection technique for remote sensing images acquired on the same geographical area but at different time instances is proposed by conducting Covariance Intersection(CI) to perform unsupervised fusion of the final fuzzy partition matrices from the Fuzzy C-Means(FCM) clustering for the feature space by applying compressed sampling to the given remote sensing images.The proposed approach exploits a CI-based data fusion of the membership function matrices,which are obtained by taking the Fuzzy C-Means(FCM) clustering of the frequency-domain feature vectors and spatial-domain feature vectors,aimed at enhancing the unsupervised change detection performance.Compressed sampling is performed to realize the image local feature sampling,which is a signal acquisition framework based on the revelation that a small collection of linear projections of a sparse signal contains enough information for stable recovery.The experimental results demonstrate that the proposed algorithm has a good change detection results and also performs quite well on denoising purpose.
基金supported in part by the Science and Technology Innovation 2030-Key Project of“New Generation Artificial Intelligence”(2018AAA0102303)the Young Elite Scientists Sponsorship Program of China Association of Science and Technology(YESS20210289)+1 种基金the China Postdoctoral Science Foundation(2020TQ1057,2020M682823)the National Natural Science Foundation of China(U20B2071,U1913602,91948204)。
文摘Change detection(CD)is becoming indispensable for unmanned aerial vehicles(UAVs),especially in the domain of water landing,rescue and search.However,even the most advanced models require large amounts of data for model training and testing.Therefore,sufficient labeled images with different imaging conditions are needed.Inspired by computer graphics,we present a cloning method to simulate inland-water scene and collect an auto-labeled simulated dataset.The simulated dataset consists of six challenges to test the effects of dynamic background,weather,and noise on change detection models.Then,we propose an image translation framework that translates simulated images to synthetic images.This framework uses shared parameters(encoder and generator)and 22×22 receptive fields(discriminator)to generate realistic synthetic images as model training sets.The experimental results indicate that:1)different imaging challenges affect the performance of change detection models;2)compared with simulated images,synthetic images can effectively improve the accuracy of supervised models.
基金National Natural Science Foundation of China(No.62101219)Natural Science Foundation of Jiangsu Province(Nos.BK20201026,BK20210921)+1 种基金Science Foundation of Jiangsu Normal University(No.19XSRX006)Open Research Fund of Jiangsu Key Laboratory of Resources and Environmental Information Engineering(No.JS202107)。
文摘With the rapid development of Unmanned Aerial Vehicle(UAV)technology,change detection methods based on UAV images have been extensively studied.However,the imaging of UAV sensors is susceptible to environmental interference,which leads to great differences of same object between UAV images.Overcoming the discrepancy difference between UAV images is crucial to improving the accuracy of change detection.To address this issue,a novel unsupervised change detection method based on structural consistency and the Generalized Fuzzy Local Information C-means Clustering Model(GFLICM)was proposed in this study.Within this method,the establishment of a graph-based structural consistency measure allowed for the detection of change information by comparing structure similarity between UAV images.The local variation coefficient was introduced and a new fuzzy factor was reconstructed,after which the GFLICM algorithm was used to analyze difference images.Finally,change detection results were analyzed qualitatively and quantitatively.To measure the feasibility and robustness of the proposed method,experiments were conducted using two data sets from the cities of Yangzhou and Nanjing.The experimental results show that the proposed method can improve the overall accuracy of change detection and reduce the false alarm rate when compared with other state-of-the-art change detection methods.
文摘People have an inherent tenacity to throng coastal regions in pursuit of better living conditions. As such the brisk dynamism of land use/land cover activities in a coastal region becomes obvious. The former keeps changing rapidly due to burgeoning population. A digital change detection analysis is performed with the help of Geographic Information System (GIS) on the Remote Sensing data spanning over last 20 years, complemented by in-situ data and ground truth information. This current research briefly endeavours to find out the nature of change happening in the major three coastal cities of Papua New Guinea (PNG), namely Alotau, capital of Milnebay province;Lae, capital of Morobe province and Port Moresby, capital of Papua New Guinea. Changes in land use and land cover that took place over 20 years have been recorded using Landsat 5 thematic mapper (TM) data of 1992 and Landsat 8 operational land imager (OLI) data. Land use and land cover maps of 1992, and 2013/14, and change detection matrix of 1992-2013/14 are derived. Results show an immensely sprawling urban landscape, evincing about five times growth during 1992 to 2014. At the same time “natural forests” dwindled by 444.96 hectares in Alotau, 6977.25 hectares in Lae and “mangrove” and “grass/shrub land” decreased by 127.78 and 4859.39 hectares respectively around Port Moresby. The above changes owe to ever increasing population pressure, land tenure shift, agriculture and industrial development.
基金Supported by National Natural Science Foundation of China(Grant Nos.61403232,61327003)Shandong Provincial Natural Science Foundation of China(Grant No.ZR2014FQ025)Young Scholars Program of Shandong University,China(YSPSDU,2015WLJH30)
文摘Detection of structural changes from an opera- tional process is a major goal in machine condition moni- toring. Existing methods for this purpose are mainly based on retrospective analysis, resulting in a large detection delay that limits their usages in real applications. This paper presents a new adaptive real-time change detection algorithm, an extension of the recent research by combin- ing with an incremental sliding-window strategy, to handle the multi-change detection in long-term monitoring of machine operations. In particular, in the framework, Hil- bert space embedding of distribution is used to map the original data into the Re-producing Kernel Hilbert Space (RK_HS) for change detection; then, a new adaptive threshold strategy can be developed when making change decision, in which a global factor (used to control the coarse-to-fine level of detection) is introduced to replace the fixed value of threshold. Through experiments on a range of real testing data which was collected from an experimental rotating machinery system, the excellent detection performances of the algorithm for engineering applications were demonstrated. Compared with state-of- the-art methods, the proposed algorithm can be more suitable for long-term machinery condition monitoring without any manual re-calibration, thus is promising in modern industries.
文摘Continuous and accurate monitoring of earth surface changes over rugged terrain Himalayas is important to manage natural resources and mitigate natural hazards.Conventional techniques generally focus on per-pixel based processing and overlook the sub-pixel variations occurring especially in case of low or moderate resolution remotely sensed data.However,the existing subpixel-based change detection(SCD)models are less effective to detect the mixed pixel information at its complexity level especially over rugged terrain regions.To overcome such issues,a topographically controlled SCD model has been proposed which is an improved version of widely used per-pixel based change vector analysis(CVA)and hence,named as a subpixel-based change vector analysis(SCVA).This study has been conducted over a part of the Western Himalayas using the advanced wide-field sensor(AWiFS)and Landsat-8 datasets.To check the effectiveness of the proposed SCVA,the cross-validation of the results has been done with the existing neural network-based SCD(NN-SCD)and per-pixel based models such as fuzzybasedCVA(FCVA)andpost-classification comparison(PCC).The results have shown that SCVA offered robust performance(85.6%-86.4%)as comparedtoNN-SCD(81.6%-82.4%),PCC(79.2%-80.4%),and FCVA(81.2%-83.6%).We concluded that SCVA helps in reducing the detection of spurious pixels and improve the efficacy of generating change maps.This study is beneficial for the accurate monitoring of glacier retreat and snow cover variability over rugged terrain regions using moderate resolution remotely sensed datasets.
基金supported by the National Natural Science Foundation of China(41774006)the Comparative Study of Geo-environment and Geohazards in the Yangtze River Delta and the Red River Delta Projectthe Shanghai Science and Technology Development Foundation(20dz1201200)。
文摘Constrained by complex imaging mechanism and extraordinary visual appearance,change detection with synthetic aperture radar(SAR)images has been a difficult research topic,especially in urban areas.Although existing studies have extended from bi-temporal data pair to multi-temporal datasets to derive more plentiful information,there are still two problems to be solved in practical applications.First,change indicators constructed from incoherent feature only cannot characterize the change objects accurately.Second,the results of pixel-level methods are usually presented in the form of the noisy binary map,making the spatial change not intuitive and the temporal change of a single pixel meaningless.In this study,we propose an unsupervised man-made objects change detection framework using both coherent and incoherent features derived from multi-temporal SAR images.The coefficients of variation in timeseries incoherent features and the man-made object index(MOI)defined with coherent features are first combined to identify the initial change pixels.Afterwards,an improved spatiotemporal clustering algorithm is developed based on density-based spatial clustering of applications with noise(DBSCAN)and dynamic time warping(DTW),which can transform the initial results into noiseless object-level patches,and take the cluster center as a representative of the man-made object to determine the change pattern of each patch.An experiment with a stack of 10 TerraSAR-X images in Stripmap mode demonstrated that this method is effective in urban scenes and has the potential applicability to wide area change detection.
文摘The study is aimed at analyzing the risk of Taita Hills region of harmful runoff and soil erosion by employing morphometric analysis and change detection in a GIS environment to prioritize the Taita Hills in Taita Taveta County. The objective of the study was to characterize and give hierarchy in which the region should be conserved. The methodology adopted hydrological modeling, morphometric computation, Weighted Sum Analysis (WSA) and change detection. Hydrological modeling was vital in delineating the sub-watersheds and stream network. Morphometric computation and WSA was applicable in coming up with parameters and weighting the parameters for each sub-watershed’s prioritization. Change detection is related to how human activity is important for conservation as the effect of land forms and dimensions are compounded. Twenty-one fourth order streamed sub-watersheds were generated and prioritized using morphometry and change detection. Every sub-watershed is given a hierarchy based on the calculated compound parameter from the WSA equation developed and shows the risk of runoff and soil erosion. The morphometric prioritization shows 47% of the watersheds are in the high and very highly susceptible areas and there are two sub-watersheds with the highest land cover change. As well six sub-watersheds are risky with both land cover change and morphometry.
基金Project supported by the National Science Foundation of China (No.49771063)
文摘Because of quick development of cities, the update of urban GIS data is very important. Change detection is the base of automatic or semi-automatic data update. One way of change detections in urban area is based on old and new aerial images acquired in different durations. The corresponding theory and experiments are introduced and analyzed in this paper. The main procedure includes four stages. The new and old images have to be registered firstly. Then image matching, based on the maximum correlation coefficient, is performed between registered images after the low contrast areas have been removed. The regions with low matching quality are extracted as candidate changed areas. Thirdly, the Gaussian-Laplacian operator is used to detect edges in candidate changed areas on both the registered images, and the straight lines are detected by Hough transformation. Finally, the changed houses and roads can be detected on the basis of straight line matching in candidate changed areas between registered images.
文摘Mapping rice cultivation is indispensable for monitoring food supply conditions in Bangladesh because of the economical importance of the crop for supporting ever increasing population in the country. In this paper, we extract the rice paddy field using the MODIS satellite data for five districts of Pabna, Manikganj, Sherpur, Sylhet, and Gazipur, each of which is characterized with its own aspects in terms of rice cultivation. Land classification is implemented using the vegetation index information derived from the red (band 1) and near-infrared (band 2) bands of MODIS 8-day composite time series data for the two time periods of 2001-2003 and 2011-2013. Results of unsupervised classification indicate that the paddy area coverage increased about 4% and 1% in Gazipur and Sylhet, respectively. In Pabna, Manikganj, and Sherpur, on the other hand, paddy area decreased by 10%, 2% and 5%, respectively, whereas notable increase of 12%, 2% and 7% was found in homestead area coverage, which is becoming more and more important for better management of small-scale agroforestry. At the same time, in Sherpur and Sylhet, forest area increased by 1% and 2% over the same time period. As a validation of these results, the changes detected in Gazipur are compared with those previously derived from the analysis of Landsat data with higher spatial resolution of 30 m as compared with that of MODIS (250 m). Also, the seasonal rice cropping pattern is studied in these five districts for discriminating cultivated rice types. These changes suggest that as a whole, efforts are being made to increase the food production, though the influence of population pressure and economic growth is apparent in these regions.
基金Project supported by the National Natural Science Foundation of China(No.49525101)
文摘The change of house often brings on the change of DSM in an area over different periods. If we can apply information of the height of houses to reinforce the house change detection, the reliability and efficiency of detection methods will be improved greatly.From this viewpoint, a new approach taking advantage of both height data from an image pair and image data is proposed to detect the house change in urban area and is called 'data fusion technology'.
文摘Land use/land cover (LULC) changes have become a central issue in current global change and sustainability research. Saudi Arabia has undergone significant change in land use and land cover since the government embarked on a course of intense national development 30 years ago, as a result of huge national oil revenues. This study evaluates LULC change in Makkah and Al-Taif, Saudi Arabia from 1986 to 2013 using Landsat images. Maximum likelihood and object-oriented classification were used to develop LULC maps. The change detection was executed using post-classification comparison and GIS. The results indicated that urban areas have increased over the period by approximately 174% in Makkah and 113% in Al-Taif. Analysis of vegetation cover over the study area showed a variable distribution from year to year due to changing average precipitation in this environment. Object-based classification provided slightly greater accuracy than maximum likelihood classification. Information provided by satellite remote sensing can play an important role in quantifying and understanding the relationship between population growth and LULC changes, which can assist future planning and potential environmental impacts of expanding urban areas.
文摘This paper presents a framework for road network change detection in order to update the Canadian National Topographic DataBase (NTDB). The methodology has been developed on the basis of road extraction from IRS\|pan images (with a 5.8 m spatial resolution) by using a wavelet approach. The feature matching and conflation techniques are used to road change detection and updating. Elementary experiments have showed that the proposed framework could be used for developing an operational road database updating system.
文摘The problems and difficulty of current change detection techniques are presented. Then, according to whether image registration is done before change detection algorithms, the authors classify the change detection into two categories:the change detection after image registration and the change detection simultaneous with image registration. For the former, four topics including the change detection between new image and old image, the change detection between new image and old map, the change detection between new image/old image and old map, and the change detection between new multi-source images and old map/image are introduced. For the latter, three categories, i.e. the change detection between old DEM, DOM and new non-rectification image, the change detection between old DLG, DRG and new non-rectification image, and the 3D change detection between old 4D products and new multi-overlapped photos, are discussed.
基金supported by the National Natural Science Foundation of China (NSFC) (No.30571112).
文摘Due to inappropriate planning and management, accelerated urban growth and tremendous loss in land, especially cropland, have become a great challenge for sustainable urban development in China, especially in developed urban area in the coastal regions; therefore, there is an urgent need to effectively detect and monitor the land use changes and provide accurate and timely information for planning and management. In this study a method combining principal component analysis (PCA) of multisensor satellite images from SPOT (systeme pour l'observation de la terre or earth observation satellite)-5 multispectral (XS) and Landsat-7 enhanced thematic mapper (ETM) panchromatic (PAN) data, and supervised classification was used to detect and analyze the dynamics of land use changes in the city proper of Hangzhou. The overall accuracy of the land use change detection was 90.67% and Kappa index was 0.89. The results indicated that there was a considerable land use change (10.03% of the total area) in the study area from 2001 to 2003, with three major types of land use conversions: from cropland into built-up land, construction site, and water area (fish pond). Changes from orchard land into built-up land were also detected. The method described in this study is feasible and useful for detecting rapid land use change in the urban area.