Extensive identification of landslide boundaries using remote sensing images and deep learning method

The frequent occurrence of extreme weather events has rendered numerous landslides to a global natural disaster issue.It is crucial to rapidly and accurately determine the boundaries of landslides for geohazards evaluation and emergency response.Therefore,the Skip Connection DeepLab neural network(SCDnn),a deep learning model based on 770 optical remote sensing images of landslide,is proposed to improve the accuracy of landslide boundary detection.The SCDnn model is optimized for the over-segmentation issue which occurs in conventional deep learning models when there is a significant degree of similarity between topographical geomorphic features.SCDnn exhibits notable improvements in landslide feature extraction and semantic segmentation by combining an enhanced Atrous Spatial Pyramid Convolutional Block(ASPC)with a coding structure that reduces model complexity.The experimental results demonstrate that SCDnn can identify landslide boundaries in 119 images with MIoU values between 0.8and 0.9;while 52 images with MIoU values exceeding 0.9,which exceeds the identification accuracy of existing techniques.This work can offer a novel technique for the automatic extensive identification of landslide boundaries in remote sensing images in addition to establishing the groundwork for future inve stigations and applications in related domains.

Classification and Spatio-Temporal Change Detection of Land Use/Land Cover Using Remote Sensing and Geographic Information System in the Manouba Region, NE Tunisia

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.

A transformer-based Siamese network and an open optical dataset for semantic change detection of remote sensing images

Recent change detection(CD)methods focus on the extraction of deep change semantic features.However,existing methods overlook the fine-grained features and have the poor ability to capture long-range space–time information,which leads to the micro changes missing and the edges of change types smoothing.In this paper,a potential transformer-based semantic change detection(SCD)model,Pyramid-SCDFormer is proposed,which precisely recognizes the small changes and fine edges details of the changes.The SCD model selectively merges different semantic tokens in multi-head self-attention block to obtain multiscale features,which is crucial for extraction information of remote sensing images(RSIs)with multiple changes from different scales.Moreover,we create a well-annotated SCD dataset,Landsat-SCD with unprecedented time series and change types in complex scenarios.Comparing with three Convolutional Neural Network-based,one attention-based,and two transformer-based networks,experimental results demonstrate that the Pyramid-SCDFormer stably outperforms the existing state-of-the-art CD models and obtains an improvement in MIoU/F1 of 1.11/0.76%,0.57/0.50%,and 8.75/8.59%on the LEVIR-CD,WHU_CD,and Landsat-SCD dataset respectively.For change classes proportion less than 1%,the proposed model improves the MIoU by 7.17–19.53%on Landsat-SCD dataset.The recognition performance for small-scale and fine edges of change types has greatly improved.

Correg-Yolov3:a Method for Dense Buildings Detection in High-resolution Remote Sensing Images

The exploration of building detection plays an important role in urban planning,smart city and military.Aiming at the problem of high overlapping ratio of detection frames for dense building detection in high resolution remote sensing images,we present an effective YOLOv3 framework,corner regression-based YOLOv3(Correg-YOLOv3),to localize dense building accurately.This improved YOLOv3 algorithm establishes a vertex regression mechanism and an additional loss item about building vertex offsets relative to the center point of bounding box.By extending output dimensions,the trained model is able to output the rectangular bounding boxes and the building vertices meanwhile.Finally,we evaluate the performance of the Correg-YOLOv3 on our self-produced data set and provide a comparative analysis qualitatively and quantitatively.The experimental results achieve high performance in precision(96.45%),recall rate(95.75%),F1 score(96.10%)and average precision(98.05%),which were 2.73%,5.4%,4.1%and 4.73%higher than that of YOLOv3.Therefore,our proposed algorithm effectively tackles the problem of dense building detection in high resolution images.

Multi-class change detection of remote sensing images based on class rebalancing

Multi-class change detection can make various ground monitoring projects more efficient and convenient.With the development of deep learning,the multi-class change detection methods have introduced Deep Neural Network(DNN)to improve the accuracy and efficiency of traditional methods.The class imbalance in the image will affect the feature extraction effect of DNN.Existing deep learning methods rarely consider the impact of data on DNN.To solve this problem,this paper proposes a class rebalancing algorithm based on data distribution.The algorithm iteratively trains the SSL model,obtains the distribution of classes in the data,then expands the original dataset according to the distribution of classes,and finally trains the baseline SSL model using the expanded dataset.The trained semantic segmentation model is used to detect multi-class changes in two-phase images.This paper is the first time to introduce the image class balancing method in the multi-class change detection task,so a control experiment is designed to verify the effectiveness and superiority of this method for the unbalanced data.The mIoU of the class rebalancing algorithm in this paper reaches 0.4615,which indicates that the proposed method can effectively detect ground changes and accurately distinguish the types of ground changes.

An Intelligent Detection Method for Optical Remote Sensing Images Based on Improved YOLOv7

To address the issue of imbalanced detection performance and detection speed in current mainstream object detection algorithms for optical remote sensing images,this paper proposes a multi-scale object detection model for remote sensing images on complex backgrounds,called DI-YOLO,based on You Only Look Once v7-tiny(YOLOv7-tiny).Firstly,to enhance the model’s ability to capture irregular-shaped objects and deformation features,as well as to extract high-level semantic information,deformable convolutions are used to replace standard convolutions in the original model.Secondly,a Content Coordination Attention Feature Pyramid Network(CCA-FPN)structure is designed to replace the Neck part of the original model,which can further perceive relationships between different pixels,reduce feature loss in remote sensing images,and improve the overall model’s ability to detect multi-scale objects.Thirdly,an Implicitly Efficient Decoupled Head(IEDH)is proposed to increase the model’s flexibility,making it more adaptable to complex detection tasks in various scenarios.Finally,the Smoothed Intersection over Union(SIoU)loss function replaces the Complete Intersection over Union(CIoU)loss function in the original model,resulting in more accurate prediction of bounding boxes and continuous model optimization.Experimental results on the High-Resolution Remote Sensing Detection(HRRSD)dataset demonstrate that the proposed DI-YOLO model outperforms mainstream target detection algorithms in terms of mean Average Precision(mAP)for optical remote sensing image detection.Furthermore,it achieves Frames Per Second(FPS)of 138.9,meeting fast and accurate detection requirements.

Analysis of Land Use Change and Driving Force of Bole City Based on Remote Sensing Image

[Objectives] The land use change and its influence has been the frontier and hotspot in the research of the surface process of change. The aim of this study was to provide a reasonable scientific basis for the more reasonable use of regional land resources of Bole City by study of land use change and driving force of Bole City.[Methods] Through geometric correction, image mosaic and image registration processing and classification of the remote sensing images of Bole City in 2006, 2011 and 2016, the three images of land use change in land use types （land use change range, dynamic degree and variation degree） were studied, and the natural and social economy in terms of the driving forces of land use change were analyzed.[Results] In the 2006 to 2016 period, cultivated land of Bole City had the land use dynamic growth state, and the average growth rate was 0.26%; and forest land, construction land, water, grassland and unused land showed a decreasing trend, decreased by 0.23%, 0.22%, 0.75%, 3.85% and 1.52%, respectively. In the entire study period, the change of grassland was the biggest, the changes of unused land and water were the second, and the changes of cultivated land, construction land and forest land were lesser.[Conclusions] The main driving factors that effected on land use change of the study area were climate, industrialization, urbanization, social and economic activities, adjustment of agricultural structure and population expansion.

Shadow Detection Method Based on HMRF with Soft Edges for High-Resolution Remote-Sensing Images

Shadow detection is a crucial task in high-resolution remote-sensing image processing. Various shadow detection methods have been explored during the last decades. These methods did improve the detection accuracy but are still not robust enough to get satisfactory results for failing to extract enough information from the original images. To take full advantage of various features of shadows, a new method combining edges information with the spectral and spatial information is proposed in this paper. As known, edge is one of the most important characteristics in the high-resolution remote-sensing images. Unfortunately, in shadow detection, it is a high-risk strategy to determine whether a pixel is the edge or not strictly because intensity values on shadow boundaries are always between those in shadow and non-shadow areas. Therefore, a soft edge description model is developed to describe the degree of each pixel belonging to the edges or not. Sequentially, the soft edge description is incorporating to a fuzzy clustering procedure based on HMRF (Hidden Markov Random Fields), in which more appropriate spatial contextual information can be used. More concretely, it consists of two components: the soft edge description model and an iterative shadow detection algorithm. Experiments on several remote sensing images have shown that the proposed method can obtain more accurate shadow detection results.

A Summary of Change Detection Technology of Remotely-Sensed Image

This paper will describe three aspects of change detection technology of remotely-sensed images. At first, the process of change detection is presented. Then, the author makes a summary of several common change detection methods and a brief review of the advantages and disadvantages of them. At the end of this paper, the applications and difficulty of current change detection techniques are discussed.

Towards complex scenes: A deep learning-based camouflaged people detection method for snapshot multispectral images

Camouflaged people are extremely expert in actively concealing themselves by effectively utilizing cover and the surrounding environment. Despite advancements in optical detection capabilities through imaging systems, including spectral, polarization, and infrared technologies, there is still a lack of effective real-time method for accurately detecting small-size and high-efficient camouflaged people in complex real-world scenes. Here, this study proposes a snapshot multispectral image-based camouflaged detection model, multispectral YOLO(MS-YOLO), which utilizes the SPD-Conv and Sim AM modules to effectively represent targets and suppress background interference by exploiting the spatial-spectral target information. Besides, the study constructs the first real-shot multispectral camouflaged people dataset(MSCPD), which encompasses diverse scenes, target scales, and attitudes. To minimize information redundancy, MS-YOLO selects an optimal subset of 12 bands with strong feature representation and minimal inter-band correlation as input. Through experiments on the MSCPD, MS-YOLO achieves a mean Average Precision of 94.31% and real-time detection at 65 frames per second, which confirms the effectiveness and efficiency of our method in detecting camouflaged people in various typical desert and forest scenes. Our approach offers valuable support to improve the perception capabilities of unmanned aerial vehicles in detecting enemy forces and rescuing personnel in battlefield.

Land Use Land Cover Analysis for Godavari Basin in Maharashtra Using Geographical Information System and Remote Sensing

The dynamic transformation of land use and land cover has emerged as a crucial aspect in the effective management of natural resources and the continual monitoring of environmental shifts. This study focused on the land use and land cover (LULC) changes within the catchment area of the Godavari River, assessing the repercussions of land and water resource exploitation. Utilizing LANDSAT satellite images from 2009, 2014, and 2019, this research employed supervised classification through the Quantum Geographic Information System (QGIS) software’s SCP plugin. Maximum likelihood classification algorithm was used for the assessment of supervised land use classification. Seven distinct LULC classes—forest, irrigated cropland, agricultural land (fallow), barren land, shrub land, water, and urban land—are delineated for classification purposes. The study revealed substantial changes in the Godavari basin’s land use patterns over the ten-year period from 2009 to 2019. Spatial and temporal dynamics of land use/cover changes (2009-2019) were quantified using three Satellite/Landsat images, a supervised classification algorithm and the post classification change detection technique in GIS. The total study area of the Godavari basin in Maharashtra encompasses 5138175.48 hectares. Notably, the built-up area increased from 0.14% in 2009 to 1.94% in 2019. The proportion of irrigated cropland, which was 62.32% in 2009, declined to 41.52% in 2019. Shrub land witnessed a noteworthy increase from 0.05% to 2.05% over the last decade. The key findings underscored significant declines in barren land, agricultural land, and irrigated cropland, juxtaposed with an expansion in forest land, shrub land, and urban land. The classification methodology achieved an overall accuracy of 80%, with a Kappa Statistic of 71.9% for the satellite images. The overall classification accuracy along with the Kappa value for 2009, 2014 and 2019 supervised land use land cover classification was good enough to detect the changing scenarios of Godavari River basin under study. These findings provide valuable insights for discerning land utilization across various categories, facilitating the adoption of appropriate strategies for sustainable land use in the region.

Using Remote Sensing to Analyse the Role of Urbanization in the Transformation of Land-Use and Land Cover in Likasi Town (Haut-Katanga, DR Congo)

Environmental change is characterized as an alteration in the environment caused primarily by human activities and ecological processes that are natural. Given the fact that the southern part of the province of Haut-Katanga in the Democratic Republic of the Congo (DRC) is part of the African Copperbelt and has been a region of intense mining for decades, humans have affected the physical environment in various ways: such as overpopulation, suburbanization, wastage, deforestation. It is therefore important to track and control the changes in the area’s mining activities. This study aimed to analyze these changes using remote sensing techniques. Landsat satellite images from 2002 and 2022 were processed and classified to quantify changes in built-up area, agricultural land, and vegetation cover over the 20-year period. The classification results revealed sizable differences between the two time points, indicating considerable expansion of built-up land and declines in agricultural land and vegetation cover from 2002 to 2022 in Likasi. These findings provide evidence that urban growth has transformed the landscape in Likasi, likely at the expense of farmland and ecosystems. Further analysis of the remote sensing data could quantify the changes and model future trends to support sustainable land use planning. The land cover and land use analysis were performed with the assistance of the ERDAS 16.6.13 software by mapping LANDSAT data from two different years 2002 and 2022.

Responses of Alpine Wetlands to Climate Changes on the Qinghai-Tibetan Plateau Based on Remote Sensing

The alpine wetlands in QTP(Qinghai-Tibetan Plateau)have been profoundly impacted along with global climate changes.We employ satellite datasets and climate data to explore the relationships between alpine wetlands and climate changes based on remote sensing data.Results show that:1)the wetland NDVI(Normalized Difference Vegetation Index)and GPP(Gross Primary Production)were more sensitive to air temperature than to precipitation rate.The wetland ET(evapotranspiration)across alpine wetlands was greatly correlated with precipitation rate.2)Alpine wetlands responses to climate changes varied spatially and temporally due to different geographic environments,variety of wetland formation and human disturbances.3)The vegetation responses of the Zoige wetland was the most noticeable and related to the temperature,while the GPP and NDVI of the Qiangtang Plateau and Gyaring-Ngoring Lake were significantly correlated with both temperature and precipitation.4)ET in the Zoige wetland showed a significantly positive trend,while ET in Maidika wetland and the Qiangtang plateau showed a negative trend,implying wetland degradation in those two wetland regions.The complexities of the impacts of climate changes on alpine wetlands indicate the necessity of further study to understand and conserve alpine wetland ecosystems.

Investigating the Use of Remote Sensing and GIS Techniques to Detect Land Use and Land Cover Change: A Review

The accuracy of change detection on the earth’s surface is important for understanding the relationships and interactions between human and natural phenomena. Remote Sensing and Geographic Information Systems (GIS) have the potential to provide accurate information regarding land use and land cover changes. In this paper, we investigate the major techniques that are utilized to detect land use and land cover changes. Eleven change detection techniques are reviewed. An analysis of the related literature shows that the most used techniques are post-classification comparison and principle component analysis. Post-classification comparison can minimize the impacts of atmospheric and sensor differences between two dates. Image differencing and image ratioing are easy to implement, but at times they do not provide accurate results. Hybrid change detection is a useful technique that makes full use of the benefits of many techniques, but it is complex and depends on the characteristics of the other techniques such as supervised and unsupervised classifications. Change vector analysis is complicated to implement, but it is useful for providing the direction and magnitude of change. Recently, artificial neural networks, chi-square, decision tree and image fusion have been frequently used in change detection. Research on integrating remote sensing data and GIS into change detection has also increased.

Automatic Change Detection of Geo-spatial Data from Imagery

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.

Edge detection method for remote sensing image based on morphological variable structuring element

There are problems such as incomplete edges and poor noise suppression when a single fixed morphological structuring element is used to detect the edges in remote sensing images. For this reason, a morphological edge detection method for remote sensing image based on variable structuring element is proposed. Firstly, the structuring elements with different scales and multiple directions are constructed according to the diversity of remote sensing imagery targets. In order to suppress the noise of the target background and highlight the edge of the image target in the remote sensing image by adaptive Top hat and Bottom hat transform, the corresponding adaptive morphological operations are constructed based on variable structuring elements; Secondly, adaptive morphological edge detection is used to obtain multiple images with different scales and directional edge features; Finally, the image edges are obtained by weighted summation of each direction edge, and then the least square is used to fit the edges for accurate location of the edge contour of the target. The experimental results show that the proposed method not only can detect the complete edge of remote sensing image, but also has high edge detection accuracy and superior anti-noise performance. Compared with classical edge detection and the morphological edge detection with a fixed single structuring element, the proposed method performs better in edge detection effect, and the accuracy of detection can reach 95 %

Road Vector Map Change Monitoring Based on High Resolution Remote Sensing Images

Some studies about road vector map change detection were done in this paper. Firstly, on the basis of old road vector data, the original high resolution remote sensing image was cut into segments. Then, gray analysis and edge extraction of those segments were done so that changes of roads could be detected. Finally, according to the vector data and gray information of roads which were not changed, road templates were extracted and saved automatically. This method was performed on the World View high resolution image of certain parts in the country. The detection result shows that detection correctness is 79.56% and completeness can reach 97.72%. Moreover, the extracted road templates are essentials for the template matching method of road extraction.

Analysis of Spatio-Temporal Changes of Land Usein Xuzhou City Based on Remote Sensing

Based on the satellite remote sensing TM/ETM images of Xuzhou city, basic data about land use of the city from 1994 to 2000 are obtained with the neural network classification module of PCI software, and the dynamic con- version matrix of land use is thus calculated. The areas of construction land and water body have increased by 1833.93 hm2 and 804.87 hm2, respectively. On the contrary, the area of cropland has decreased by 3207.24 hm2. The area of cropland converted into construction land makes up 26.84%, and that converted into water body amounts for 8.17% of the total area of cropland in 1994. The variation index of land use degree and the dynamic degree index of land use computed are 1.38 and 57.81%, respectively, which demonstrate that land use in Xuzhou is in a development period and the changes are drastic. The frequency index and importance index of the form in which cropland converted into con- struction land are 29.91% and 68.93% respectively. The results indicate that the change is not only widespread in space but a major form of spatial change of land use in the area.

Object Detection in Remote Sensing Images Using Picture Fuzzy Clustering and MapReduce

In image processing, one of the most important steps is image segmentation. The objects in remote sensing images often have to be detected in order toperform next steps in image processing. Remote sensing images usually havelarge size and various spatial resolutions. Thus, detecting objects in remote sensing images is very complicated. In this paper, we develop a model to detectobjects in remote sensing images based on the combination of picture fuzzy clustering and MapReduce method (denoted as MPFC). Firstly, picture fuzzy clustering is applied to segment the input images. Then, MapReduce is used to reducethe runtime with the guarantee of quality. To convert data for MapReduce processing, two new procedures are introduced, including Map_PFC and Reduce_PFC.The formal representation and details of two these procedures are presented in thispaper. The experiments on satellite image and remote sensing image datasets aregiven to evaluate proposed model. Validity indices and time consuming are usedto compare proposed model to picture fuzzy clustering model. The values ofvalidity indices show that picture fuzzy clustering integrated to MapReduce getsbetter quality of segmentation than using picture fuzzy clustering only. Moreover,on two selected image datasets, the run time of MPFC model is much less thanthat of picture fuzzy clustering.

Monitoring of Temporal and Spatial Changes of Land Use and Land Cover in Metropolitan Regions through Remote Sensing and GIS

The use of remote sensing techniques and subsequent analysis by means of geographical information system (GIS) offers an effective method for monitoring temporal and spatial changes of landscapes. This work studies the urbanization processes and associated threats to natural ecosystems and resources in the metropolitan areas of Berlin and Erlangen-Fürth-Nürnber?Schwabach (EFNS). To compute the land use/cover (LULC) of the study areas, a supervised classification of “maximum likelihood” using Landsat data for the years of 1972, 1985, 1998, 2003, and 2015 is used. Results show that the built-up area is the dominant land use in both regions throughout the study period. This land use has increased at the expense of green and open areas in EFNS and at the expense of agricultural land in Berlin. Likewise, 5% of forest in EFNS is replaced with urban infrastructure. However, the amount of forest in Berlin increased by 3%. While EFNS experienced relatively big changes in its water bodies from 1972 to 1985, changes in water bodies in Berlin were rather slight during the last 40 years. The overall accuracy of our remotely sensed LULC maps was between 88% and 94% in Berlin and between 85.87% and 87.4% for EFNS. The combination of remote sensing and GIS appears to be an indispensable tool for monitoring changes in LULC in urban areas and help improving LU planning to avoid environmental and ecological problems.