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SAUNet3+CD: A Siamese-Attentive UNet3+for change detection in Remote Sensing Images

IEEE ACCESS 2022年 10卷 101434-101444页

作者： Mo, Junsang Seong, Seonkyeong Oh, Jaehong Choi, Jaewan Korea Res Inst Human Settlements Geospatially Enabled Soc Res Div Sejong 30147 South Korea Chungbuk Natl Univ Dept Civil Engn Cheongju 28644 South Korea Korea Maritime & Ocean Univ Dept Civil Engn Interdisciplinary Major Ocean Renewable Energy En Busan 49112 South Korea

With the development of various optical sensors, change detection is one of the most actively researched areas in remotely sensed imagery with high spatial resolution. In particular, deep learning-based change detection techniques are very important for use in various fields, such as land monitoring and disaster analysis, because they can show superior performance compared to traditional unsupervised and supervised change detection methods. This manuscript proposes a Siamese-attentive UNet3+ for change detection (SAUNet3+CD) of multitemporal imagery with high spatial resolution. The existing UNet3+ was modified to a Siamese-based architecture, and a spatial and channel attention module was added to detect various changed areas. The proposed model was trained to effectively detect building growth and decay through the data augmentation of open datasets and a hybrid loss function. In experiments using two open datasets, the proposed deep learning model effectively detected changed areas in multitemporal images better than various methods, such as existing Siamese-based networks and a network for semantic segmentation.

关键词： Semantics Feature extraction Remote sensing Convolutional neural networks Deep learning Spatial resolution detection algorithms change detection algorithms change detection attention module deep learning UNet3+ Siamese-based architecture

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CTMNet: Enhanced Open-Pit Mine Extraction and change detection With a Hybrid CNN-Transformer Multitask Network

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷

作者： Xing, Jianghe Zhang, Jue Li, Jun Gao, Yongsheng Du, Shouhang Zhang, Chengye Wang, Yanheng China Univ Min & Technol Beijing Coll Geosci & Surveying Engn Beijing 100083 Peoples R China Minist Nat Resources Key Lab China ASEAN Satellite Remote Sensing Appli Nanning 530221 Peoples R China Griffith Univ Inst Integrated & Intelligent Syst Nathan Qld 4111 Australia Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 150001 Peoples R China Harbin Engn Univ Qingdao Innovat & Dev Base Qingdao 266000 Peoples R China

Automatic open-pit mine extraction and change detection from high-resolution remote sensing images are of great importance to mineral resource management. However, the high spatial heterogeneity and spectral variations of mining area scenarios make these tasks challenging. Motivated by the strong correlation between the two tasks and their potential mutual benefits, this article presents a hybrid convolutional neural network (CNN)-Transformer multitask network (CTMNet). Constructed in an encoder-decoder manner, CTMNet has two sperate extraction paths (EPs) to localize the regions of interest for bi-temporal images, along with a change detection path (CDP) to identify discrepancies by differentiating the multiscale feature representations from the EPs. As the basic building block for the EP, a CNN-Transformer hybrid block is designed to enhance the global and local feature representation capacity. To cope with the variations in the bi-temporal images, we propose the feature alignment module for the CDP. A hard sample mining-based contrastive constraint loss is proposed to emphasize the contributions of hard samples to the training process. The experimental results on a collected open-pit mine extraction and change detection dataset (OMECSet) and two public datasets reveal the validity of the CTMNet when compared to the state-of-the-art methods. The OMECSet and the code of CTMNet have been made public available at https://***/s/80519cb980ca54456447.

关键词： Feature extraction Remote sensing Accuracy Shape Transformers Data mining Machine learning algorithms Distortion change detection algorithms Electronic mail change detection contrastive constraint feature alignment object extraction open-pit mine remote sensing

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Quickest change detection for Unnormalized Statistical Models

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IEEE TRANSACTIONS ON INFORMATION THEORY 2024年第2期70卷 1220-1232页

作者： Wu, Suya Diao, Enmao Banerjee, Taposh Ding, Jie Tarokh, Vahid Duke Univ Dept Elect & Comp Engn Durham NC 27708 USA Univ Pittsburgh Dept Ind Engn Pittsburgh PA 15213 USA Univ Minnesota Sch Stat Minneapolis MN 55455 USA

Classical quickest change detection algorithms require modeling pre-change and post-change distributions. Such an approach may not be feasible for various machine learning models because of the complexity of computing the explicit distributions. Additionally, these methods may suffer from a lack of robustness to model mismatch and noise. This paper develops a new variant of the classical Cumulative Sum (CUSUM) algorithm for the quickest change detection. This variant is based on Fisher divergence and the Hyvarinen score and is called the Hyvarinen score-based CUSUM (SCUSUM) algorithm. The SCUSUM algorithm allows the applications of change detection for unnormalized statistical models, i.e., models for which the probability density function contains an unknown normalization constant. The asymptotic optimality of the proposed algorithm is investigated by deriving expressions for average detection delay and the mean running time to a false alarm. Numerical results are provided to demonstrate the performance of the proposed algorithm.

关键词： change detection algorithms Delays Computational modeling detection algorithms Machine learning Random variables Numerical models Quickest change detection CUSUM fisher divergence score matching unnormalized models

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Unsupervised Deep Slow Feature Analysis for change detection in Multi-Temporal Remote Sensing Images

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2019年第12期57卷 9976-9992页

作者： Du, Bo Ru, Lixiang Wu, Chen Zhang, Liangpei Wuhan Univ Sch Comp Sci Wuhan Peoples R China Wuhan Univ Collaborat Innovat Ctr Geospatial Technol Wuhan 430072 Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430072 Peoples R China

change detection has been a hotspot in the remote sensing technology for a long time. With the increasing availability of multi-temporal remote sensing images, numerous change detection algorithms have been proposed. Among these methods, image transformation methods with feature extraction and mapping could effectively highlight the changed information and thus has a better change detection performance. However, the changes of multi-temporal images are usually complex, and the existing methods are not effective enough. In recent years, the deep network has shown its brilliant performance in many fields, including feature extraction and projection. Therefore, in this paper, based on the deep network and slow feature analysis (SFA) theory, we proposed a new change detection algorithm for multi-temporal remotes sensing images called deep SFA (DSFA). In the DSFA model, two symmetric deep networks are utilized for projecting the input data of bi-temporal imagery. Then, the SFA module is deployed to suppress the unchanged components and highlight the changed components of the transformed features. The change vector analysis pre-detection is employed to find unchanged pixels with high confidence as training samples. Finally, the change intensity is calculated with chi-square distance and the changes are determined by threshold algorithms. The experiments are performed on two real-world data sets and a public hyperspectral data set. The visual comparison and the quantitative evaluation have shown that DSFA could outperform the other state-of-the-art algorithms, including other SFA-based and deep learning methods.

关键词： Feature extraction Remote sensing change detection algorithms detection algorithms Eigenvalues and eigenfunctions Artificial neural networks Training change detection deep network remote sensing images slow feature analysis (SFA)

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Sequential detection of Multiple change Points in Networks: A Graphical Model Approach

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IEEE TRANSACTIONS ON INFORMATION THEORY 2013年第9期59卷 5824-5841页

作者： Amini, Arash Ali XuanLong Nguyen Univ Michigan Dept Stat Ann Arbor MI 48109 USA

We propose a probabilistic formulation that enables sequential detection of multiple change points in a network setting. We present a class of sequential detection rules for certain functionals of change points (minimum among a subset), and prove their asymptotic optimality in terms of expected detection delay. Drawing from graphical model formalism, the sequential detection rules can be implemented by a computationally efficient message-passing protocol which may scale up linearly in network size and in waiting time. The effectiveness of our inference algorithm is demonstrated by simulations.

关键词： change detection algorithms distributed algorithms graphical models pattern recognition statistical learning

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Research on the Application Model of Sensing Technology in Monitoring Landscape Resource changes

Research on the Application Model of Sensing Technology in M...

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Electronics, Energy Systems and Power Engineering (EESPE), IEEE International Conference on

作者： Weiqun Lei Yan Huang Nanning University Nanning China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

This paper proposes an innovative landscape resource change detection algorithm based on multi-sensor fusion. The algorithm integrates multiple data sources such as remote sensing images, ground meteorological data and climate data, uses data fusion technology to improve monitoring accuracy, and combines spatiotemporal filtering algorithm to accurately detect landscape changes. This paper selects a nature reserve as the experimental area, collects multi-source data sets including remote sensing images, meteorological data and ground sensor data, and detects changes in landscape resources through the algorithm. Compared with the traditional method, the detection accuracy of the proposed algorithm is improved by 12.6%, especially when dealing with changes under complex environmental conditions, it shows strong robustness. In addition, the real-time performance of the algorithm has also been optimized, which can adapt to large-scale, complex and changeable monitoring tasks. The multi-sensor fusion algorithm proposed has significant advantages over the traditional single sensor method in terms of accuracy, stability and real-time performance through experiments.

关键词： Accuracy change detection algorithms Soft sensors Real-time systems Stability analysis Robustness Spatiotemporal phenomena Monitoring Remote sensing Meteorology

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THE ASYMPTOTIC LOCAL APPROACH TO change detection AND MODEL VALIDATION

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 1987年第7期32卷 583-592页

作者： BENVENISTE, A BASSEVILLE, M MOUSTAKIDES, GV CNRS INST RECH INFORMAT & SYST ALEATOIRESRENNESFRANCE

We present a systematic approach for the design of change detection and model validation algorithms for dynamical systems. We show how to associate to any identification algorithm a change detection and a model validation procedure, which are optimal in some asymptotic sense. The foundations of our method go back to the asymptotic local approach in statistics, and our method generalizes this approach.

关键词： Signal processing algorithms change detection algorithms Testing Statistics Automatic control Adaptive algorithm Monitoring Error correction Algorithm design and analysis Heuristic algorithms

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Autocorrelation-Based Quickest change detection

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IEEE COMMUNICATIONS LETTERS 2020年第12期24卷 2913-2916页

作者： Afser, Huseyin Yabaci, Seyhun Barbaros Adana Alparslan Turke Sci & Technol Univ Dept Elect & Elect Engn TR-01250 Adana Turkey Cukurova Univ Dept Elect & Elect Engn TR-01330 Adana Turkey

We consider the utilization of the autocorrelation information for aiding the quickest detection problem. Specifically, we investigate the problem of quickly detecting a Gaussian source with autocorrelation such that some of its symbols are repeated as cyclic prefixes. Based on the cumulative sum algorithm, we propose a method which takes advantage of this autocorrelation in order to provide performance improvement compared to the classical energy based detection of the uncorrelated source.

关键词： Correlation Signal to noise ratio Delays OFDM AWGN change detection algorithms Probability Quickest detection cumulative sum algorithm autocorrelation detection energy detection

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PPCNET: A Combined Patch-Level and Pixel-Level End-to-End Deep Network for High-Resolution Remote Sensing Image change detection

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2020年第10期17卷 1797-1801页

作者： Bao, Tengfei Fu, Chenqin Fang, Tao Huo, Hong Shanghai Jiao Tong Univ Dept Automat Shanghai Peoples R China Shanghai Jiao Tong Univ Coll Elect Informat & Elect Engn Shanghai Peoples R China

Extracting change regions from bitemporal images is crucial to urban planning, land, and resources survey. In the literature, many methods obtaining difference between bitemporal remote sensing images have been proposed. However, there are still some problems due to the complexity of change conditions. In order to solve the above-mentioned problems, we propose a novel network called PPCNET, combining patch-level and pixel-level change detection for bitemporal remote sensing images. This network is divided into three branches: the dual structure is used to extract features of bitemporal images, respectively;changed or unchanged image regions are then detected through fully connected layers, and a soft-max layer at patch level. Once a change is detected at patch level, feature encoder and decoder at pixel level are activated to obtain accurate change boundary. Furthermore, a feature pyramid network-based architecture is employed to fuse information in different layers to further improve change detection effectiveness. Experiments on both satellite and aerial remote sensing images have verified that PPCNET network yields higher change detection accuracy with faster detection speed.

关键词： Feature extraction Remote sensing Satellites Training Convolution change detection algorithms Decoding Bitemporal images change or unchange pairs combining patch-level and pixel-level information pixel-patch combination net

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A Unified Approach to change detection Using an Adaptive Ensemble of Extreme Learning Machines

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2020年第5期17卷 794-798页

作者： Khurana, Madhu Saxena, Vikas Jaypee Inst Informat Technol Dept Comp Sci Engn & Informat Technol Noida 201309 India

change detection for remote sensing (RS) images is a challenging task. The variation in the spatial, radiometric, spectral, and temporal resolution of the images adds to the complexity of the change-detection process. The application domain also has an impact on the decision to use a particular change-detection technique. There is no generic classification algorithm, which can be used for different application domains using the RS images like green cover, land use, forest fires, and so on. This letter proposes an adaptive ensemble of extreme learning machines (ELMs) for classification of RS images into change/no-change classes. ELM has good generalization capability and a fast learning phase. Therefore, an adaptive ensemble of different ELMs has been proposed. The proposed algorithm has been implemented in five sets of data. It has been used for earth monitoring applications, viz. green cover, flat fires, water bodies, and so on. Here the results for two data sets, viz. Sardinia Island and Mexico Fire have been presented. The results thus obtained are highly promising. They show an average accuracy of 90.5% in detecting the change.

关键词： Training Feature extraction Testing Earth Image reconstruction Remote sensing change detection algorithms change detection extreme learning machines (ELMs) remote sensing (RS) images

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