An unsupervised change-detection method that considers the spatial contextual information in a log-ratio difference image generated from multitemporal SAR images is proposed. A Markov random filed (MRF) model is parti...An unsupervised change-detection method that considers the spatial contextual information in a log-ratio difference image generated from multitemporal SAR images is proposed. A Markov random filed (MRF) model is particularly employed to exploit statistical spatial correlation of intensity levels among neighboring pixels. Under the assumption of the independency of pixels and mixed Gaussian distribution in the log-ratio difference image, a stochastic and iterative EM-MPM change-detection algorithm based on an MRF model is developed. The EM-MPM algorithm is based on a maximiser of posterior marginals (MPM) algorithm for image segmentation and an expectation-maximum (EM) algorithm for parameter estimation in a completely automatic way. The experiment results obtained on multitemporal ERS-2 SAR images show the effectiveness of the proposed method.展开更多
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 ...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.展开更多
Incremental image compression techniques using priori information are of significance to deal with the explosively increasing remote-sensing image data. However, the potential benefi ts of priori information are still...Incremental image compression techniques using priori information are of significance to deal with the explosively increasing remote-sensing image data. However, the potential benefi ts of priori information are still to be evaluated quantitatively for effi cient compression scheme designing. In this paper, we present a k-nearest neighbor(k-NN) based bypass image entropy estimation scheme, together with the corresponding mutual information estimation method. Firstly, we apply the k-NN entropy estimation theory to split image blocks, describing block-wise intra-frame spatial correlation while avoiding the curse of dimensionality. Secondly, we propose the corresponding mutual information estimator based on feature-based image calibration and straight-forward correlation enhancement. The estimator is designed to evaluate the compression performance gain of using priori information. Numerical results on natural and remote-sensing images show that the proposed scheme obtains an estimation accuracy gain by 10% compared with conventional image entropy estimators. Furthermore, experimental results demonstrate both the effectiveness of the proposed mutual information evaluation scheme, and the quantitative incremental compressibility by using the priori remote-sensing frames.展开更多
In this paper,we build a remote-sensing satellite imagery priori-information data set,and propose an approach to evaluate the robustness of remote-sensing image feature detectors.The building TH Priori-Information(TPI...In this paper,we build a remote-sensing satellite imagery priori-information data set,and propose an approach to evaluate the robustness of remote-sensing image feature detectors.The building TH Priori-Information(TPI)data set with 2297 remote sensing images serves as a standardized high-resolution data set for studies related to remote-sensing image features.The TPI contains 1)raw and calibrated remote-sensing images with high spatial and temporal resolutions(up to 2 m and 7 days,respectively),and 2)a built-in 3-D target area model that supports view position,view angle,lighting,shadowing,and other transformations.Based on TPI,we further present a quantized approach,including the feature recurrence rate,the feature match score,and the weighted feature robustness score,to evaluate the robustness of remote-sensing image feature detectors.The quantized approach gives general and objective assessments of the robustness of feature detectors under complex remote-sensing circumstances.Three remote-sensing image feature detectors,including scale-invariant feature transform(SIFT),speeded up robust features(SURF),and priori information based robust features(PIRF),are evaluated using the proposed approach on the TPI data set.Experimental results show that the robustness of PIRF outperforms others by over 6.2%.展开更多
The use of unmanned aerial vehicles(UAV)for forest monitoring has grown significantly in recent years,providing information with high spatial resolution and temporal versatility.UAV with multispectral sensors allow th...The use of unmanned aerial vehicles(UAV)for forest monitoring has grown significantly in recent years,providing information with high spatial resolution and temporal versatility.UAV with multispectral sensors allow the use of indexes such as the normalized difference vegetation index(NDVI),which determines the vigor,physiological stress and photo synthetic activity of vegetation.This study aimed to analyze the spectral responses and variations of NDVI in tree crowns,as well as their correlation with climatic factors over the course of one year.The study area encompassed a 1.6-ha site in Durango,Mexico,where Pinus cembroides,Pinus engelmannii,and Quercus grisea coexist.Multispectral images were acquired with UAV and information on meteorological variables was obtained from NASA/POWER database.An ANOVA explored possible differences in NDVI among the three species.Pearson correlation was performed to identify the linear relationship between NDVI and meteorological variables.Significant differences in NDVI values were found at the genus level(Pinus and Quercus),possibly related to the physiological features of the species and their phenology.Quercus grisea had the lowest NDVI values throughout the year which may be attributed to its sensitivity to relative humidity and temperatures.Although the use of UAV with a multispectral sensor for NDVI monitoring allowed genera differentiation,in more complex forest analyses hyperspectral and LiDAR sensors should be integrated,as well other vegetation indexes be considered.展开更多
提出了一种顾及空间邻域关系的多时相SAR影像非监督变化检测方法,采用马尔可夫随机场(Markov random field,MRF)模型描述SAR比值差异图像的空间上下文信息,提出了基于该模型的EM-MPM非监督变化检测算法。实例研究表明,与未顾及空间上下...提出了一种顾及空间邻域关系的多时相SAR影像非监督变化检测方法,采用马尔可夫随机场(Markov random field,MRF)模型描述SAR比值差异图像的空间上下文信息,提出了基于该模型的EM-MPM非监督变化检测算法。实例研究表明,与未顾及空间上下文信息的EM双阈值算法相比,该方法能够有效地提高变化区域提取的可靠性和准确性。展开更多
文摘An unsupervised change-detection method that considers the spatial contextual information in a log-ratio difference image generated from multitemporal SAR images is proposed. A Markov random filed (MRF) model is particularly employed to exploit statistical spatial correlation of intensity levels among neighboring pixels. Under the assumption of the independency of pixels and mixed Gaussian distribution in the log-ratio difference image, a stochastic and iterative EM-MPM change-detection algorithm based on an MRF model is developed. The EM-MPM algorithm is based on a maximiser of posterior marginals (MPM) algorithm for image segmentation and an expectation-maximum (EM) algorithm for parameter estimation in a completely automatic way. The experiment results obtained on multitemporal ERS-2 SAR images show the effectiveness of the proposed method.
文摘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.
基金supported by National Basic Research Project of China(2013CB329006)National Natural Science Foundation of China(No.61622110,No.61471220,No.91538107)
文摘Incremental image compression techniques using priori information are of significance to deal with the explosively increasing remote-sensing image data. However, the potential benefi ts of priori information are still to be evaluated quantitatively for effi cient compression scheme designing. In this paper, we present a k-nearest neighbor(k-NN) based bypass image entropy estimation scheme, together with the corresponding mutual information estimation method. Firstly, we apply the k-NN entropy estimation theory to split image blocks, describing block-wise intra-frame spatial correlation while avoiding the curse of dimensionality. Secondly, we propose the corresponding mutual information estimator based on feature-based image calibration and straight-forward correlation enhancement. The estimator is designed to evaluate the compression performance gain of using priori information. Numerical results on natural and remote-sensing images show that the proposed scheme obtains an estimation accuracy gain by 10% compared with conventional image entropy estimators. Furthermore, experimental results demonstrate both the effectiveness of the proposed mutual information evaluation scheme, and the quantitative incremental compressibility by using the priori remote-sensing frames.
基金the National Key Research and Development Program of China under Grant 2018YFF0301205in part by the National Natural Science Foundation of China under Grant NSFC 61925105 and Grant 61801260.
文摘In this paper,we build a remote-sensing satellite imagery priori-information data set,and propose an approach to evaluate the robustness of remote-sensing image feature detectors.The building TH Priori-Information(TPI)data set with 2297 remote sensing images serves as a standardized high-resolution data set for studies related to remote-sensing image features.The TPI contains 1)raw and calibrated remote-sensing images with high spatial and temporal resolutions(up to 2 m and 7 days,respectively),and 2)a built-in 3-D target area model that supports view position,view angle,lighting,shadowing,and other transformations.Based on TPI,we further present a quantized approach,including the feature recurrence rate,the feature match score,and the weighted feature robustness score,to evaluate the robustness of remote-sensing image feature detectors.The quantized approach gives general and objective assessments of the robustness of feature detectors under complex remote-sensing circumstances.Three remote-sensing image feature detectors,including scale-invariant feature transform(SIFT),speeded up robust features(SURF),and priori information based robust features(PIRF),are evaluated using the proposed approach on the TPI data set.Experimental results show that the robustness of PIRF outperforms others by over 6.2%.
基金supported by the National Council of Science and Technology of Mexico(CONACyT),which provided financial support through scholarships for postgraduate studies to J.L.G.S.(815176)and M.R.C.(507523)。
文摘The use of unmanned aerial vehicles(UAV)for forest monitoring has grown significantly in recent years,providing information with high spatial resolution and temporal versatility.UAV with multispectral sensors allow the use of indexes such as the normalized difference vegetation index(NDVI),which determines the vigor,physiological stress and photo synthetic activity of vegetation.This study aimed to analyze the spectral responses and variations of NDVI in tree crowns,as well as their correlation with climatic factors over the course of one year.The study area encompassed a 1.6-ha site in Durango,Mexico,where Pinus cembroides,Pinus engelmannii,and Quercus grisea coexist.Multispectral images were acquired with UAV and information on meteorological variables was obtained from NASA/POWER database.An ANOVA explored possible differences in NDVI among the three species.Pearson correlation was performed to identify the linear relationship between NDVI and meteorological variables.Significant differences in NDVI values were found at the genus level(Pinus and Quercus),possibly related to the physiological features of the species and their phenology.Quercus grisea had the lowest NDVI values throughout the year which may be attributed to its sensitivity to relative humidity and temperatures.Although the use of UAV with a multispectral sensor for NDVI monitoring allowed genera differentiation,in more complex forest analyses hyperspectral and LiDAR sensors should be integrated,as well other vegetation indexes be considered.
文摘提出了一种顾及空间邻域关系的多时相SAR影像非监督变化检测方法,采用马尔可夫随机场(Markov random field,MRF)模型描述SAR比值差异图像的空间上下文信息,提出了基于该模型的EM-MPM非监督变化检测算法。实例研究表明,与未顾及空间上下文信息的EM双阈值算法相比,该方法能够有效地提高变化区域提取的可靠性和准确性。
