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SpectralSpatial-Aware Unsupervised change detection With Stochastic Distances and Support Vector Machines

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2021年第4期59卷 2863-2876页

作者： Negri, Rogerio Galante Frery, Alejandro C. Casaca, Wallace Azevedo, Samara Dias, Mauricio Araujo Silva, Erivaldo Antonio Alcantara, Enner Herenio Univ Estadual Paulista UNESP Sci & Technol Inst Dept Environm Engn BR-12245000 Sao Jose Dos Campos Brazil Univ Fed Alagoas Lab Comp Cient & Anal Numer BR-57072900 Maceio Alagoas Brazil Xidian Univ Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China Univ Estadual Paulista UNESP Dept Energy Engn BR-19274000 Rosana Brazil Univ Fed Itajuba UNIFEI Dept Nat Resources BR-35903087 Itajuba Brazil Univ Estadual Paulista UNESP Dept Math & Comp Sci Sch Sci & Technol BR-19060900 Presidente Prudente Brazil Univ Estadual Paulista UNESP Sch Sci & Technol Dept Cartog BR-19060900 Presidente Prudente Brazil

change detection is a topic of great interest in remote sensing. A good similarity metric to compute the variations among the images is the key to high-quality change detection. However, most existing approaches rely on the fixed threshold values or the user-provided ground truth in order to be effective. The inability to deal with artificial objects such as clouds and shadows is a significant difficulty for many change-detection methods. We propose a new unsupervised change-detection framework to address those critical points. The notion of homogeneous regions is introduced together with a set of geometric operations and statistic-based criteria to characterize and distinguish formally the change and nonchange areas in a pair of remote sensing images. Moreover, a robust and statistically well-posed family of stochastic distances is also proposed, which allows comparing the probability distributions of different regions/objects in the images. These stochastic measures are then used to train a support-vector-machine-based approach in order to detect the change/nonchange areas. Three study cases using the images acquired with different sensors are given in order to compare the proposed method with other well-known unsupervised methods.

关键词： Support vector machines Remote sensing Robustness Stochastic processes Measurement change detection algorithms Principal component analysis Classification single-class support vector machine (SVM) stochastic distance unsupervised change detection

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change-Point detection for High-Dimensional Time Series With Missing Data

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2013年第1期7卷 12-27页

作者： Xie, Yao Huang, Jiaji Willett, Rebecca Duke Univ Dept Elect & Comp Engn Durham NC 27706 USA

This paper describes a novel approach to change-point detection when the observed high-dimensional data may have missing elements. The performance of classical methods for change-point detection typically scales poorly with the dimensionality of the data, so that a large number of observations are collected after the true change-point before it can be reliably detected. Furthermore, missing components in the observed data handicap conventional approaches. The proposed method addresses these challenges by modeling the dynamic distribution underlying the data as lying close to a time-varying low-dimensional submanifold embedded within the ambient observation space. Specifically, streaming data is used to track a submanifold approximation, measure deviations from this approximation, and calculate a series of statistics of the deviations for detecting when the underlying manifold has changed in a sharp or unexpected manner. The approach described in this paper leverages several recent results in the field of high-dimensional data analysis, including subspace tracking with missing data, multiscale analysis techniques for point clouds, online optimization, and change-point detection performance analysis. Simulations and experiments highlight the robustness and efficacy of the proposed approach in detecting an abrupt change in an otherwise slowly varying low-dimensional manifold.

关键词： Statistical learning signal processing algorithms signal detection change detection algorithms

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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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SAR Image change detection Based on Heterogeneous Graph With Multiattributes and Multirelationships

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IEEE ACCESS 2022年 10卷 44347-44361页

作者： Wang, Jun Zhang, Anjun Quzhou Univ Sch Mech Engn Quzhou 324000 Zhejiang Peoples R China Anhui Univ Sch Internet Hefei 230000 Anhui Peoples R China Anhui Prov Key Lab Ind Safety & Emergency Technol Hefei 230000 Anhui Peoples R China

The performance of change detection between synthetic aperture radar (SAR) images mainly depends on the selection and utilization of image attributes. Nevertheless, most existing change detection approaches merely take the intensity attribute into consideration, constraining their capacities of detecting changes in complex situations. To solve this problem, this study develops an unsupervised SAR image change detection approach based on heterogeneous graph with multi-attributes and multi-relationships. First, the structural attribute, obtained by removing the complex texture, can be used to reflect the overall features of a SAR image. A heterogeneous graph is then constructed to encode the structural and intensity attributes as vertices, and two types of edges are hence connected to capture the intra-relationships and inter-relationships from these different attributes. With the support of this graph, the hyper-adjacency characteristics are proposed to quantify the multi-relationships and describe the global heterogeneous information. Constructing heterogeneous graphs respectively on bi-temporal SAR images, the changes of both intensity and structural attributes can be measured via comparing the hyper-adjacency characteristics of the bi-temporal graphs, thereby generating a difference image with good separability. Finally, the change map is obtained by cutting off the weakly related edges of heterogeneous graph on the difference image. Experiments on four real SAR datasets prove the effectiveness of the proposed graph-driven approach in improving the accuracy and robustness of change detection.

关键词： Radar polarimetry Speckle Synthetic aperture radar change detection algorithms Image edge detection Task analysis Licenses change detection synthetic aperture radar heterogeneous graph multi-attributes multi-relationships hyper-adjacency characteristic

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Bayesian Quickest change detection for Active Sensors

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IEEE COMMUNICATIONS LETTERS 2016年第11期20卷 2229-2232页

作者： Sukumaran, Vineeth Bala Indian Inst Space Sci & Technol Dept Avion Thiruvananthapuram 695547 Kerala India

We consider the energy efficient quickest change detection problem for active sensors, which use radar, ultrasound, or optical sensing to control the information content in the sequentially collected noisy data. Controlling the extent of information contained in the collected data would lead to a cost or energy expenditure, where the cost would naturally increase as the extent of contained information increases. For such a sensor, our objective is to design control policies that optimally trade off performance metrics, such as detection delay, average cost expended in control, and probability of false alarm. We consider this problem in the setting of Bayesian quickest change detection. We propose two simple heuristic threshold policies, for which the total cost is shown to be close to the optimal through simulations and numerical studies.

关键词： Intelligent sensors change detection algorithms dynamic programming

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Data Augmentation and Few-Shot change detection in Forest Remote Sensing

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2023年 16卷 5919-5934页

作者： Zhu, Songyu Jing, Weipeng Kang, Peilun Emam, Mahmoud Li, Chao Northeast Forestry Univ Coll Informat & Comp Engn Harbin 150040 Peoples R China Collegeof Elect & Informat Engn Harbin Vocat & TechnicalCollege China Harbin 150081 Peoples R China Northeast Forestry Univ Coll Informat & Comp Engn Harbin Peoples R China Menoufia Univ Fac Artificial Intelligence Shibin Al Kawm Egypt

Forest remote sensing change detection provides an important technical support for forest management decisions and analysis of forest disturbance factors. However, lack of data in specialized fields leads to the detection accuracy-improved difficulty. In this study, a forest remote sensing change detection model in the context of few-shot learning is proposed. The proposed model achieves end-to-end change detection algorithm for forest scenes from two perspectives of data augmentation and updated few-shot algorithm. First, forest fragments images are jointly generated by using feature extraction network and generative adversarial network. Then, forest fragments are blended into the original change detection dataset by Poisson blending method to achieve effective augmentation. Furthermore, the end-to-end change detection network is also updated using a few-shot learning. In addition, Metalearning module is added to the slow feature analysis algorithm based on the multiattention mechanism to realize the detection effect improvements. The proposed model improves the F1 score from 86% to 91% on the two datasets. Moreover, it increases the F1 score by 6.52% on average.

关键词： Forestry Remote sensing Feature extraction Data models change detection algorithms Training Deep learning Few-shot learning forest change detection Index Terms remote sensing data augmentation slow feature analysis

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Group Self-Paced Learning With a Time-Varying Regularizer for Unsupervised change detection

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2020年第4期58卷 2481-2493页

作者： Gong, Maoguo Duan, Yingying Li, Hao Xidian Univ Minist Educ China Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China

Unsupervised change detection based on supervised or semisupervised classifiers has achieved strong adaptability and robustness to obtain satisfactory change detection results. However, these methods suffer from an issue that it is hard to collect reliable training samples in an unsupervised manner. In this article, a group self-paced learning (GSPL) framework is proposed to mine the reliable training samples. In the proposed method, each sample is assigned a weight to indicate its reliability. The proposed scheme is able to learn the weighted samples and update the weights iteratively in a self-paced manner to identify the reliable training samples. In the phase of updating weights, a grouping strategy is designed to avoid selecting training samples from homogeneous regions. Furthermore, a novel time-varying self-paced regularizer is proposed to automatically determine the learning scheme of self-paced learning. Finally, three classifiers, including SoftMax, backpropagation neural network, and support vector machine, are investigated under this proposed framework. Experiments on five change detection data sets demonstrate that the proposed framework can significantly outperform those state-of-art methods for change detection in terms of accuracy and robustness.

关键词： Training change detection algorithms Robustness Support vector machines Remote sensing Feature extraction change detection remote sensing image self-paced learning (SPL)

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Sensitivity Analysis for L-Band Polarimetric Descriptors and Fusion for Urban Land Cover change detection

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2014年第10期7卷 4231-4242页

作者： Mishra, Bhogendra Susaki, Junichi Kyoto Univ Dept Civil & Earth Resources Engn Geoinformat Lab Kyoto 6158540 Japan

A fully polarimetric synthetic aperture radar (PolSAR) image allows the generation of a number of polarimetric descriptors. These descriptors are sensitive to changes in land use and cover. Thus, the objective of this study is twofold: first, to identify the most effective descriptors for each change type and ascertain the best complementary pairs from the selected polarimetric descriptors;and second, to develop an information fusion approach to use the unique features found in each polarimetric descriptor to obtain a better change map for urban and suburban environments. The effectiveness of each descriptor was assessed through statistical analysis of the sensitivity index in selected areas and through change detection results obtained by using the supervised thresholding method. A good agreement was found between the statistical analysis and the performance of each descriptor. Finally, a polarimetric information fusion method based on the coupling of modified thresholding with a region-growing algorithm was implemented for the identified complementary descriptor pairs. The mapping accuracy, as measured by the Kappa coefficient, was improved by 0.09 (from 0.76 to 0.85) with a significant reduction of false and missing alarm rates compared to using single PolSAR images.

关键词： change detection algorithms polarimetric descriptors synthetic aperture radar (SAR) urban changes

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Toward Generalized change detection on Planetary Surfaces With Convolutional Autoencoders and Transfer Learning

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2019年第10期12卷 3900-3918页

作者： Kerner, Hannah Rae Wagstaff, Kiri L. Bue, Brian D. Gray, Patrick C. Bell, James F., III Ben Amor, Heni Univ Maryland Dept Geog Sci College Pk MD 20742 USA CALTECH Jet Prop Lab 4800 Oak Grove Dr Pasadena CA 91109 USA Duke Univ Nicholas Sch Environm Durham NC 27710 USA Arizona State Univ Sch Earth & Space Explorat Tempe AZ 85282 USA Arizona State Univ Sch Comp Informat & Decis Syst Engn Tempe AZ 85282 USA

Ongoing planetary exploration missions are returning large volumes of image data. Identifying surface changes in these images, e.g., new impact craters, is critical for investigating many scientific hypotheses. Traditional approaches to change detection rely on image differencing and manual feature engineering. These methods can be sensitive to irrelevant variations in illumination or image quality and typically require before and after images to be coregistered, which itself is a major challenge. Additionally, most prior change detection studies have been limited to remote sensing images of earth. We propose a new deep learning approach for binary patch-level change detection involving transfer learning and nonlinear dimensionality reduction using convolutional autoencoders. Our experiments on diverse remote sensing datasets of Mars, the moon, and earth show that our methods can detect meaningful changes with high accuracy using a relatively small training dataset despite significant differences in illumination, image quality, imaging sensors, coregistration, and surface properties. We show that the latent representations learned by a convolutional autoencoder yield the most general representations for detecting change across surface feature types, scales, sensors, and planetary bodies.

关键词： change detection algorithms earth machine learning mars moon neural networks remote sensing supervised learning unsupervised learning

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Unsupervised change detection Based on a Unified Framework for Weighted Collaborative Representation With RDDL and Fuzzy Clustering

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2019年第11期57卷 8890-8903页

作者： Yang, Gang Li, Heng-Chao Wang, Wei-Ye Yang, Wen Emery, William J. Southwest Jiaotong Univ Sichuan Prov Key Lab Informat Coding & Transmiss Chengdu 610031 Sichuan Peoples R China Wuhan Univ Sch Elect Informat Wuhan 430079 Hubei Peoples R China Univ Colorado Dept Aerosp Engn Sci Boulder CO 80309 USA

In this paper, we propose a novel unsupervised change detection method of remote sensing (RS) images based on a unified framework for weighted collaborative representation (WCR) with robust deep dictionary learning (RDDL) and fuzzy clustering. Specifically, WCR is employed to collaboratively represent neighborhood features with lower computational complexity, for which the RDDL model is built to learn more effective and representative overcomplete dictionary and enhance the robustness against the noise and outliers. Meanwhile, in order to make the resulting collaborative coefficients more beneficial for clustering, the unified framework for WCR with RDDL and fuzzy clustering is designed. By doing so, our framework not only precludes the utilization of third-party clustering algorithm, but also achieves better detection performance. Subsequently, the spatial constraint is enforced on the membership matrix to yield the updated one for further improving the accuracy of change detection. Finally, a binary change mask (CM) is achieved by assigning the pixels into the changed and unchanged classes. Experiments are performed on five pairs of RS images, and experimental results demonstrate the effectiveness of the proposed method.

关键词： Collaboration Dictionaries Clustering algorithms change detection algorithms Feature extraction Machine learning Principal component analysis Collaborative representation deep learning 23 (DL) dictionary learning fuzzy clustering remote sensing (RS) 24 image unsupervised change detection

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