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Multitemporal SAR Images change detection Considering Ambiguous Co-Registration Errors: A Unified Framework

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2024年 21卷 1页

作者： Zhu, Jingxing Zhou, Guangyao Wang, Feng Liu, Rui Xiang, Yuming Wang, Wenzhi You, Hongjian Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China Chinese Acad Sci Key Lab Technol Geo Spatial Informat Proc & Applic Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China

Image registration and change detection are two important tasks in synthetic aperture radar (SAR) image processing. Conventional researches considered them as two individual problems, where change detection requires pixel-wise registration first. However, they can be integrated into one processing framework and benefit from each other. This letter proposes a unified algorithm to address the change detection problem in multitemporal SAR images considering ambiguous co-registration errors. Based on the framework of optical flow, we designed an advanced objective function by introducing three individual features of heterogeneous, homogeneous, and changed areas in multitemporal SAR images. Afterward, the objective function is iteratively optimized, resulting in pixel-wise correspondences and a changed map. Experimental results on multitemporal SAR images show that the proposed algorithm achieves good performances both on registration accuracy and change detection discrimination.

关键词： Radar polarimetry Optical flow Speckle Noise Linear programming Optimization Feature extraction change detection algorithms Brightness Filtering SAR change detection structure feature synthetic aperture radar (SAR) image registration

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A Nonparametric Estimator for Coherent change detection: The Permutational change detection

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Costa, Giovanni Monti-Guarnieri, Andrea Virgilio Manzoni, Marco Rucci, Alessio Politecn Milan Dipartimento Elettron Informaz & Bioingn I-20133 Milan Italy TRE ALTAMIRA Srl I-20143 Milan Italy

Nowadays, synthetic aperture radar (SAR) is widely used in heterogeneous fields with aims strictly dependent on the objectives of the application. One of the most common is the exploitation of the interferometric-SAR (InSAR) to measure millimeter movements on the Earth's surface, aiming to monitor failures (e.g., landslides) or to measure the health state of infrastructures (e.g., mining assets, bridges, and buildings). In this article, developing algorithms to detect temporal and spatial changes in the radar targets becomes very important. This article focuses on the temporal change detection framework, proposing a nonparametric coherent change detection (CCD) algorithm called permutational change detection (PCD), a purely statistical algorithm whose core is the permutational test. The PCD estimates the temporal change points (CPs) of a radar target recognizing blocks structure in the coherence matrix, namely, new radar objects. The algorithm has been fine-tuned for small SAR datasets, with the specific aim of prioritizing the analysis of the latest changes. A rigorous mathematical derivation of the algorithm is carried out, explaining how some limits have been addressed. Then, the performance analysis on the simulated data is deeply accomplished, carried out for the stand-alone PCD and the PCD compared with a parametric CCD algorithm based on the generalized likelihood ratio test (GLRT), and with the Omnibus and REACTIV detectors. The comparison with these other algorithms and the stand-alone performance analysis point out the robustness of the PCD in dealing with very noisy environments, even in the case of a single block. Finally, the PCD is validated by processing two Sentinel I data stacks, ascending and descending geometries, of the 2016 Central Italy earthquake.

关键词： Coherence Radar imaging Charge coupled devices change detection algorithms Decorrelation Sociology Radar detection Blocks structure change points (CPs) interferometric synthetic aperture radar (InSAR) nonparametric coherent change detection (CCD) permutational tests synthetic aperture radar (SAR)

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Bayesian Online change detection of Multimode Processes With Application to Wind Turbines

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1页

作者： Xu, Jun Zhou, Jie Wang, Kaibo Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China Goldwind Sci & Technol Co Ltd Beijing 100176 Peoples R China Tsinghua Univ Vanke Sch Publ Hlth Beijing 100084 Peoples R China

Online change detection of multimode processes is important for process monitoring and control, which aims to timely and accurately detect two types of changes: 1) mode changes and 2) parameter changes. However, the existing online methods mainly focus on one type of change and, thus, have difficulty capturing the complex change structure. Motivated by this, we propose a novel Bayesian online change detection method for multimode processes (BCD-MMP). Specifically, the complex change structure is characterized by three state variables (the latest change point (LCP), the mode of the current segment, and the indicator variable of parameter changes). When a new data point arrives, we infer the posterior distribution of the state variables. To make the inference process tractable, we develop a sampling-based inference algorithm, and a pruning strategy is also provided to improve the computation efficiency. Simulation and real case studies of wind turbine torque control process indicate that the proposed method can achieve better online change detection performance than the state-of-the-art methods.

关键词： Bayes methods Process control Wind turbines Torque control Generators change detection algorithms Process monitoring Bayesian online change detection mode-specific parameter changes multimode processes sampling-based inference wind turbine torque control

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change detection on multi-sensor imagery using mixed interleaved group convolutional network

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 133卷

作者： Tan, Kun Wang, Moyang Wang, Xue Ding, Jianwei Liu, Zhaoxian Pan, Chen Mei, Yong East China Normal Univ Key Lab Geog Informat Sci Minist Educ Shanghai 200241 Peoples R China East China Normal Univ Key Lab Spatial Temporal Big Data Anal & Applicat Minist Nat Resources Shanghai 200241 Peoples R China Second Surveying & Mapping Inst Hebei Shijiazhuang 050037 Peoples R China Shanghai Municipal Inst Surveying & Mapping Shanghai 200063 Peoples R China AMS Inst Def Engn Beijing 100036 Peoples R China

The difference of the spatio-spectral features of multi-sensor image causes big difficulty in change detection because of the difficulties of the feature extraction. Unlike the traditional approaches that mainly relying on manually feature design, the advances of deep learning-based methods in deep feature extraction provide new alternatives for multi-sensor imagery change detection. Specifically, the incorporation of multi-scale information from remote sensing images holds paramount importance in change detection, consistently applied in the design of various deep learning models. This study investigated a change detection approach utilizing a mixed interleaved group convolutional network (MIGCNet) on multi-sensor remote sensing imagery, with a specific focus on fine-grained kernel space and multi-scale feature analysis within convolution operations. The proposed MIGCNet, with parallel branches as the fundamental architecture, can distinguish the change information effectively by the proposed mixed interleaved group convolution (MIGC) module, which combined mixed convolution with interleaved group convolution. Meanwhile, multi-loss supervision is utilized to promote the performance of the proposed MIGCNet. Experimental results demonstrate the outperformance of the MIGCNet to handle change detection with multi-sensor images on urban area. Considering different datasets, the Overall Accuracy and Kappa Coefficient are reaching 0.97 and 80.67%, respectively, and the miss detection rate and the false alarm rate are as low as 0.17 and 0.18, respectively.

关键词： change detection algorithms Multi-loss supervision Mixed convolution Interleaved group convolution Multi sensor imagery

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SAR Image change detection Based on Data Optimization and Self-Supervised Learning

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IEEE ACCESS 2020年 8卷 217290-217305页

作者： Meng, Wenhui Wang, Liejun Du, Anyu Li, Yongming Xinjiang Univ Coll Informat Sci & Engn Urumqi 830046 Peoples R China Xinjiang Univ Key Lab Signal Detect & Proc Xinjiang Uygur Auto Urumqi 830046 Peoples R China

In the SAR change detection algorithm based on self-supervised learning, speckle noise reduces the difference image (DI) quality. Therefore, the contrast of the DI is low, and its change area is not significant. Moreover, the preclassification algorithm with the poor robustness makes the classification results of the low-quality DI inaccurate. When the wrong labels are sent into the classification network, the accuracy of the final detection results is reduced. First, to improve the quality of the initial DI, we design an adaptive gamma correction algorithm that adjusts the contrast according to the mean value of the initial DI and the variation coefficient beta. The contrast of the new DI generated by this algorithm is higher. Furthermore, to suppress the noise, we adopt a new algorithm based on popular ranking to obtain the saliency map of the new DI. Combining the initial DI with this saliency map, a high-quality DI with a low noise level is obtained. After that, we introduce the structure tensor into the fuzzy local information c-means clustering algorithm (FLICM) to classify the DI more accurately. The new clustering algorithm improves the accuracy of preclassification, especially its hierarchical version. Besides, we use the structure tensor to generate the structure maps of the original images. Finally, according to the prior information obtained from the preclassification, we use a convolution wavelet neural network (CWNN) to supervise and train the structure maps of the original images. Experimental results show that the DI generated by us is closer to the ground-truth than other methods. Our preclassification algorithm performs better. Our algorithm shows higher detection accuracy for SAR images with strong noise than some advanced change detection algorithms.

关键词： Clustering algorithms Classification algorithms Training Hyperspectral imaging Tensors change detection algorithms Feature extraction Adaptive gamma correction convolution wavelet neural network fuzzy local information c-means clustering saliency map structure tensor

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Land Cover change detection Based on Vector Polygons and Deep Learning With High-Resolution Remote Sensing Images

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Zhang, Hui Liu, Wei Niu, Hao Yin, Pengcheng Dong, Shiling Wu, Jialin Li, Erzhu Zhang, Lianpeng Zhu, Changming Jiangsu Normal Univ Sch Geog Geomat & Planning Xuzhou 221116 Peoples R China Bur Nat Resources & Planning Xuzhou 221006 Jiangsu Peoples R China

Vector polygons are valuable survey data, serving as crucial outputs of national geographical censuses and a fundamental data source for detecting changes in geographical conditions. Current remote sensing image change detection methods rely on comparing images but overlook abundant historical vector results, struggle with model generalization, and lack adequate samples. Consequently, change detection remains a manual process primarily, unable to meet the requirements for automated and efficient monitoring of standardized geographical conditions. Hence, this article proposes a change detection method for land cover vector polygons based on high-resolution remote sensing images and deep learning. Initially, the enhanced simple linear iterative clustering (SLIC) algorithm is applied to segment dual-temporal images from identical regions. Subsequently, an annotated dataset is generated using a multiscale extraction, cropping-with-inpainting approach. Next, datasets derived from pretemporal and posttemporal images are used for training and testing, respectively, and the training set is purified by using two-classifier cross-validation. Finally, an improved object-oriented convolutional neural network (CNN) model performs fine-grained scene classification. The change rules and postprocessing method are then integrated to identify changed vector polygons. To validate the effectiveness and superiority of the proposed method, we conducted experiments on land cover change detection using datasets from two study areas. The results indicate that the proposed method achieves precision and recall rates of 91.89% and 94.44% on Dataset 1, respectively. Similarly, in Dataset 2, the precision and recall rates reach 87.59% and 91.41%, respectively. These findings demonstrate the method's efficacy in detecting changed vector polygons, reducing manual intervention, and enhancing detection efficiency.

关键词： Remote sensing Feature extraction Deep learning Object oriented modeling Training Task analysis change detection algorithms change detection deep learning remote sensing vector polygons

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Unsupervised SAR Image change detection for Few changed Area Based on Histogram Fitting Error Minimization

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷 1页

作者： Zhang, Kaiyu Lv, Xiaolei Chai, Huiming Yao, Jingchuan Chinese Acad Sci Aerosp Informat Res Inst Key Lab Technol Geospatial Informat Proc & Applic Beijing 100094 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China China Acad Railway Sci State Key Lab High Speed Railway Track Technol Beijing 100891 Peoples R China

change detection in synthetic aperture radar (SAR) images is an essential task of remote sensing image analysis. However, the thresholding procedure is the main difficulty in change detection for a few changed areas for traditional change detection methods. In this article, we propose a novel change detection method for very few changed or even none changed areas. The proposed method contains three procedures: difference image (DI) generation, thresholding, and spatial analysis. In the second procedure, a new thresholding method called histogram fitting error minimization (HFEM) is proposed for a few changed areas. HFEM is derived under the assumption that the unchanged class in the absolute-valued DI follows the half-normal distribution, and the changed class follows the Gaussian distribution. In the spatial analysis procedure, a new conditional random fields (CRF) method based on half-normal distribution is proposed to model the mutual influences among image pixels. The proposed CRF method is called half-normal CRF (HNCRF). Experiments carried out on both synthetic datasets and four real SAR datasets demonstrate the superiority of our method. Not only a few changed datasets but datasets with lots of changes are used in the experiments. The kappa coefficients of the proposed method can reach up to ten times that of the traditional method under extreme conditions. The results prove that the proposed method outperforms the traditional methods in the case of a few changed areas. Meanwhile, the proposed method can get similar results compared with traditional methods under normal conditions.

关键词： Synthetic aperture radar Remote sensing Radar polarimetry change detection algorithms Histograms Rail transportation Radiometry change detection conditional random fields (CRFs) half-normal distribution image thresholding synthetic aperture radar (SAR) images unsupervised change detection

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Style Transformation-Based SpatialSpectral Feature Learning for Unsupervised change detection

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷 1页

作者： Liu, Ganchao Yuan, Yuan Zhang, Yuelin Dong, Yongsheng Li, Xuelong Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Peoples R China

Due to the inconsistent imaging environment, the styles of multitemporal multispectral images (MSIs) are quite different, such as image brightness and transparency. For multitemporal MSIs with different styles, the same object with different spectra problem is one of the biggest challenges in change detection. To overcome the challenge, a novel unsupervised spatialspectral feature learning (FL) framework based on style transformation (ST) (called STFL-CD) is proposed for MSI change detection in this article. For dual-temporal MSIs, the proposed STFl-CD algorithm consists of two phases: ST and spatialspectral FL. Since the image styles are inconsistent under different imaging environments, the first innovation is to transform the image styles through unmixing and reconstruction. Through ST, the challenge of the same object with different spectra problem will be reduced fundamentally. By introducing the attention mechanism, the other innovation is to extract the joint spectralspatial change features based on a 3-D convolutional neural network with spatial and channel attention. In addition, for multitemporal MSIs, a multitemporal version STFL-CD (MT-STFL-CD) framework is designed based on a recurrent neural network to learn the correlation features between multitemporal remote sensing images. Both of the visual and quantitative results on the real MSI datasets indicate that the proposed unsupervised STFL-CD frameworks have significant advantages on multitemporal MSI change detection. In particular, the performance of the proposed unsupervised STFL-CD algorithm is even comparable to that of the state-of-the-art supervised or semisupervised methods.

关键词： Feature extraction Remote sensing Imaging Automobiles Production facilities change detection algorithms detection algorithms Attention mechanism change detection multispectral image (MSI) style transformation (ST)

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Exploiting Variational Inequalities for Generalized change detection on Graphs

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2023年 61卷

作者： Florez-Ospina, Juan F. Jimenez-Sierra, David A. Benitez-Restrepo, Hernan D. Arce, Gonzalo R. Univ Delaware Dept Elect & Comp Engn Newark NJ 19711 USA Pontificia Univ Javeriana Dept Elect & Ciencias Comp Cali 760031 Colombia

This article introduces a unified framework for developing graph-based change detection (CD) algorithms in remote sensing, which is based on signal feasibility problems and variational inequalities. We argue that signal feasibility problems provide a natural way to frame the CD problem, while variational inequalities, core elements of modern data science and signal processing methods, enable us to find efficient, stable, and reliable solutions to the proposed feasibility problems. We demonstrate the design of both semisupervised and unsupervised CD schemes from our perspective, establishing connections with graph Laplacian filtering and graph convolutional networks (GCNs). In contrast to specialized methods that rely on composite objective functions with multiple penalty parameters, our approach greatly simplifies hyperparameter selection, as the hyperparameters are both bounded and can form convex combinations (i.e., they are nonnegative and sum up to one). We evaluate our approach on various real heterogeneous and homogeneous datasets, demonstrating its capabilities compared to traditional and modern CD methods. In addition, our ablation studies confirm the consistency of our solutions under variations in the number of nodes and graph structure learning (GSL) methods. We conclude by discussing the advantages, limitations, and promising future research directions, with connections to graph filtering, sampling set selection, and self-supervised learning.

关键词： Training Feature extraction Laplace equations Filtering Satellite images change detection algorithms Symmetric matrices Bitemporal satellite imagery change detection (CD) graph filtering graph smoothness prior model graph structure learning (GSL) label propagation (LP) signal feasibility problem untrained graph convolutional networks (GCN) variational inequality

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Application of Remote Sensing Image change detection Algorithm in Extracting Damaged Buildings in Earthquake Disaster

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IEEE ACCESS 2024年 12卷 149308-149319页

作者： Jia, Shaohui Chu, Shengguang Hou, Qiaoyi Liu, Jingyue PipeChina Inst Sci & Technol Langfang 065000 Peoples R China China Univ Petr East China Coll Comp Sci & Technol Qingdao 266580 Peoples R China

In post-earthquake reconstruction, the rapid and effective utilization of pre-disaster and post-disaster remote sensing data is crucial. In such scenarios, remote sensing satellite technology demonstrates its unique advantages by quickly and dynamically acquiring high-resolution imagery of extensive areas in earthquake-affected regions, efficiently capturing the immediate post-disaster situation. change detection technology has become a key tool through these remote sensing images. This technology can automatically identify changes in damaged areas, buildings, and other critical infrastructure through the analysis of pre-disaster and post-disaster remote sensing imagery data, aiding in post-disaster reconstruction efforts. Existing remote sensing change detection methods mainly rely on Convolutional Neural Network (CNN) or Transformer for construction. Still, these methods often fail to fully balance the advantages and disadvantages of these two technologies. They are not specifically optimized for the features of the change detection task (extracting and learning features of the changed area). To address this issue, this paper fully leverages the global information processing capabilities of the Transformer and the local information capture capabilities of CNN, proposing a multi-level feature guided aggregation network model composed of multiple branches that fully integrate the respective strengths of both. The model initially captures global information from the images using the Transformer-based main network. Subsequently, it extracts local information from the images employing a custom multi-scale strip convolution module based on CNN. Subsequently, the global and local information extracted during the encoding phase is further integrated through the feature aggregation network, and the final prediction map is generated using an attention fusion module. In the experimental section, the effectiveness of the proposed algorithm is further validated through compara

关键词： Feature extraction Transformers Data mining Remote sensing Convolutional neural networks Mathematical models Image reconstruction Accuracy Earthquakes Construction industry change detection algorithms Buildings Post-disaster reconstruction remote sensing change detection convolutional neural network (CNN) transformer

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