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SAR Image change detection in Spatial-Frequency Domain Based on Attention Mechanism and Gated Linear Unit

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2023年 20卷 1页

作者： Zhao, Chunhui Ma, Lirui Wang, Lu Ohtsuki, Tomoaki Mathiopoulos, P. Takis Wang, Yong Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Peoples R China Minist Ind & Informat Technol Key Lab Adv Marine Commun & Informat Technol Harbin 150001 Peoples R China Keio Univ Dept Informat & Comp Sci Yokohama Kanagawa 2238522 Japan Natl & Kapodistrian Univ Athens Dept Telecommun & Signal Proc Athens 15771 Greece Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China

change detection based on synthetic aperture radar (SAR) images is an important application in the remote-sensing technology field. However, the lack of labeled data has been a difficult problem in SAR image detection, especially for pixel-level change detection. In this letter, we propose a novel unsupervised change detection algorithm, which improves the detection accuracy by exploring features from both spatial and frequency domains of SAR images. In particular, first clustering is used as preclassification to obtain pseudo-labels and then by incorporating classifiers and pseudo-labels in terms of feature learning, a novel unsupervised detection algorithm is proposed. To improve the sensitivity of the algorithm to changed details and enhance the antinoise ability of the change detection network, the attention mechanism (AM) is integrated into the network to fully extract important spatial structure information. Moreover, a multidomain fusion module is proposed to integrate spatial and frequency domain features into complementary feature representations. This module contains multiregion features weighted by the channel-spatial AM and deep features filtered out by the gated linear units (GLUs) in the frequency domain. To verify the effectiveness of the proposed algorithm, it is compared against the other four SAR image change detection algorithms using three real datasets. The experimental results show that the proposed method outperforms the other four algorithms in terms of percent correct classification (PCC) and Kappa coefficient (KC).

关键词： Feature extraction Frequency-domain analysis Radar polarimetry Logic gates change detection algorithms Synthetic aperture radar Remote sensing Attention mechanism (AM) change detection gated linear unit (GLU) spatial-frequency domain synthetic aperture radar (SAR) image

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Multilevel Information Fusion-Based change detection for Multiangle PolSAR Images

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022年 19卷 1页

作者： Zou, Bin Li, Haolin Zhang, Lamei Harbin Inst Technol Dept Informat Engn Harbin 150001 Peoples R China

change detection is a key technology in the field of polarimetric synthetic aperture radar (PolSAR) image processing. The current research on the change detection mainly focuses on studying PolSAR images with the same angle or small angle difference, and the angle problem is not considered. However, when the angle difference occurs, especially a large angle difference, some pixels might be falsely detected because the angle difference can affect the polarimetric characteristics. In this letter, we propose a multilevel information fusion-based (MIFB) method, which is suitable for extracting change information from PolSAR images with angle difference. In particular, the proposed method first adopts data resolution correction, then applies an improved feature-based registration algorithm, and finally, incorporates weighted graph theory with the superpixel segmentation algorithm to extract and merge pixel-based and object-based change areas to eliminate false alarms. Experimental results for multitemporal and multiangle PolSAR images reveal that the MIFB method can effectively eliminate false detection caused by angle differences and improve the detection accuracy.

关键词： Scattering Data mining Feature extraction TV change detection algorithms Signal processing algorithms Image segmentation change detection image registration multiangle polarimetric synthetic aperture radar (PolSAR)

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A CNN Framework With Slow-Fast Band Selection and Feature Fusion Grouping for Hyperspectral Image change detection

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

作者： Ou, Xianfeng Liu, Liangzhen Tu, Bing Zhang, Guoyun Xu, Zhi Hunan Inst Sci & Technol Sch Informat Sci & Engn Yueyang 414006 Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Images & Graph Intelligent Proc Guilin 541004 Peoples R China

change detection approaches can detect changed areas of the same scene at different times. Hyperspectral remote-sensing images contain large amounts of spectral information at high resolution. As hyperspectral datasets become abundant, more and more change detection technologies use hyperspectral images as raw data. Hyperspectral images suffer from band redundancy. There is an urgent need to improve the directionality of change of information features. To solve these problems, in this article, we propose a CNN framework involving slow-fast band selection (SFBS) and feature fusion grouping (SFBS-FFGNET) for hyperspectral image change detection. The main contributions of this article are as follows: 1) based on slow feature analysis (SFA), an SFBS method is proposed, which selects slow and fast feature bands to better extract changed and unchanged features, to more effectively separate changed and unchanged pixels;2) we used a difference matrix to enrich the level of change information to provide more change characteristics for change detection;and 3) an FFG method was used to generate a more discriminative feature group, and the related loss function was designed. Experimental results on multiple real hyperspectral datasets showed that SFBS can reduce the operating load on the computer and improve the accuracy of change detection, and FFG can also improve the accuracy of change detection. In summary, SFBS-FFGNET is superior to most existing change detection methods.

关键词： Feature extraction Hyperspectral imaging Deep learning Data mining Convolutional neural networks change detection algorithms Tensors change detection difference matrix feature fusion grouping (FFG) hyperspectral image slow-fast band selection (SFBS)

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An Adaptive and Adjustable Maximum-Likelihood Estimator for SAR change detection

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷

作者： Wang, Mengmeng Zhang, Jixian Deng, Kazhong Zhuang, Huifu Hua, Fenfen China Univ Min & Technol Sch Environm & Spatial Informat Xuzhou 221116 Jiangsu Peoples R China Natl Qual Inspect & Testing Ctr Surveying & Mappi Beijing 100830 Peoples R China Jiangsu Normal Univ Sch Geog Geomat & Planning Xuzhou 221116 Jiangsu Peoples R China

Synthetic aperture radar (SAR) change detection (CD) can be broadly classified into two categories: noncoherent intensity CD and coherent change detection (CCD). The former methods, belonging to the field of image processing, are theoretically suitable for all SAR images since they only utilize the information of SAR magnitude;however, the detection precision cannot be guaranteed. The latter methods can show effective performances for most SAR image pairs using identical collection geometrics because of the basis of the probability and statistics theory. In this article, we propose a novel change estimator to combine the advantages of the two kinds of algorithms in a theoretical derivation way. An intensity-based estimator, inspired by the derivation of the coherence estimator in CCD, is first proposed. It is a new maximum-likelihood (ML) change estimator maximizing the probability distribution function (pdf) of the ratio change statistic instead of SAR complex data. In addition, the simple linear iterative cluster (SLIC) algorithm is introduced to make the new estimator adjustable because changes at different degrees can be extracted with varying settings of the superpixels number, which is further demonstrated in real SAR images. Finally, experiments in SAR image pairs of different statistical characteristics show that the proposed estimator can yield higher contrast SAR CD images than the other five common change statistics and obtain better CD maps than the other four classic thresholding methods.

关键词： Synthetic aperture radar Radar polarimetry Coherence Charge coupled devices Maximum likelihood estimation change detection algorithms Image segmentation change detection (CD) maximum-likelihood (ML) estimator synthetic aperture radar (SAR)

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An Adaptive Fuzzy Markov Random Field Model for change detection

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Chinese Journal of Electronics 2023年第3期21卷 466-470页

作者： Fei GAO Bona CHEN Jinping SUN School of Electronic and Information Engineering Beihang University Beijing China

This paper proposes a change detection algorithm based on a novel adaptive fuzzy Markov random field model. The purpose is to improve the adaptability and accuracy of change detection algorithm through a non-parametric adaptive framework. We formulate the change detection problem as a constraint optimization problem according to maximum a posterior probability criterion, and then design a non-parametric energy function which can adaptively adjust contributions of contextual information and observed data to labeling decision making. Finally, the gradient projection optimization method is applied to the scheme to obtain optimal change detection result. Theoretical analysis and experimental results show the validity of the proposed algorithm.

关键词： Constraint optimization Adaptation models change detection algorithms Decision making Stars Optimization methods Labeling

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Land-Cover change detection for SAR Images Based on Biobjective Fuzzy Local Information Clustering Method With Decomposition

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022年 19卷

作者： Fang, Wei Xi, Chao Jiangnan Univ Jiangsu Prov Engn Lab Pattern Recognit & Computat Wuxi 214122 Jiangsu Peoples R China

The existence of a speckle noise significantly affects the accuracy of land-cover change detection results for synthetic aperture radar (SAR) images. This letter proposes a biobjective fuzzy local information clustering method with decomposition (BIFLICM/D) to address this problem. SAR images change detection is described as a biobjective fuzzy local information clustering problem from the aspects of preserving image details and removing noise. To improve the ability to extract the original information detail, the log-mean ratio method is used to generate a first difference image in BIFLICM/D. The second difference image is achieved by combining the homomorphic filtering and saliency detection, which effectively removes the speckle noise. Fuzzy clustering objective functions are then constructed for the two difference images to recognize the changed and unchanged pixels under different requirements. A new fuzzy membership degree-updating method is adopted to optimize the two objective functions, which can balance the influences of the two objectives and improve the robustness of the proposed method. The experimental result demonstrates that the proposed method is more effective and superior to the comparison algorithms.

关键词： Radar polarimetry Synthetic aperture radar Task analysis Speckle Remote sensing change detection algorithms Biomedical imaging Biobjective optimization change detection fuzzy clustering synthetic aperture radar (SAR)

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In-Band Wormhole detection in Wireless Ad Hoc Networks Using change Point detection Method

In-Band Wormhole Detection in Wireless Ad Hoc Networks Using...

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Ad-Hoc and Sensor Networks Symposium

作者： Maggie X. Cheng Yi Ling Wei Biao Wu Missouri University of Science and Technology The University of Chicago

ISBN: (纸本)9781479966653

This paper addresses detecting in-band wormholes in wireless ad hoc networks. The detection scheme requires collecting the end-to-end delay of packets at the receiver and then applying a sequential change point detection algorithm to detect abrupt changes in the delay time series. A new change point detection algorithm, named SW-CLT, is proposed. The algorithm is based on the Central Limit Theorem (CLT) and does not involve using a preset detecting threshold. The algorithm is compared with the non-parametric cumulative sum (NP-CUSUM) because the non-parametric version is believed to be more robust to highly dynamic data than the parametric version. SW-CLT has the ability to adjust its detection threshold with the variance of the data, and therefore is more robust than NP-CUSUM, which uses a preset threshold. Simulation results from ns3 verified the advantage of SW-CLT over NP-CUSUM in all simulated scenarios.

关键词： Mobile ad hoc networks wireless change detection algorithms Central limit theorem wormhole detection algorithms preset inspection methods Packet detection limit

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Parallel fast Global K-Means algorithm for synthetic aperture radar image change detection using OpenCL

Parallel fast Global K-Means algorithm for synthetic apertur...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Huming Zhu Qingyu Zhang Xinying Ren Licheng Jiao Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Xidian University Xidian China

ISBN: (纸本)9781479979301

In this article, Fast Global K-Means (FGKM) for Synthetic Aperture Radar (SAR) image change detection is presented. On account of the time-consuming of FGKM algorithm and the real-time demand, we present a Parallel Fast Global K-Means (P-FGKM) algorithm. We parallelize the selection of initial cluster centers which is the most time-consuming step of FGKM algorithm. The proposed algorithm is implemented based on Open Computing Language (OpenCL). The experiments are carried out on a variety of heterogeneous computing devices, such as Multi-core CPU, GPU, Intel HD Graphics, Many Integrated Core (MIC). Experiment results show that the proposed algorithm can achieve a good speedup up to 86 times on such devices.

关键词： Clustering algorithms change detection algorithms Graphics processing units Synthetic aperture radar High definition video Kernel Algorithm design and analysis

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Adaptive change detection approach for object detection in outdoor scenes under variable speed illumination changes1

Adaptive change detection approach for object detection in o...

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European Signal Processing Conference (EUSIPCO)

作者： Lucio Marcenaro Gianluca Gera Carlo Regazzoni Dept. of Biophisical and Electronic Engineering (DIBE) - University of Genoa Via all'Opera Pia Ila 16145 Genova Italy

In this paper an adaptive algorithm is presented for the detection of changes in outdoor video-surveillance images. The proposed method can be considered as basis for a low level image processing stage in a advanced video-surveillance system. The main feature of the algorithm is the robustness to the illumination changes in the scene: this robustness is achieved by using a background updating module working in cooperation with the change detection algorithm. The background updating method works in a different way according to speed of illumination changes. If a sudden illumination change is detected the background is heavily updated while if a slow lightning variation occurs, a continuous soft updating module is used to take into account the long-term slow illumination changes. Experimental results of an automatic people counting system including the proposed low level image processing stage indicate that the method detects changes accurately in case of time-varying illumination.

关键词： Lighting Image segmentation change detection algorithms Robustness Lightning Object segmentation

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Spatio-Temporal Statistical Sequential Analysis for Temperature change detection in Satellite Imagery

Spatio-Temporal Statistical Sequential Analysis for Temperat...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Husam Alfergani Nidhal Bouaynaya Rouzbeh Nazari Rowan University Electrical and Computer Engineering NJ University of Alabama at Birmingham Civil Construction and Environmental Engineering Birmingham AL

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

The analysis of remote sensing data enables us to detect changes and monitor land surface temperature (LST). However, analysis of times series data poses some challenges, including weather conditions, seasonality and noise, that limit the effectiveness of change detection algorithms. While existing algorithms perform relatively well for detecting abrupt transitions, reliable detection of gradual changes is more difficult. In this paper, we formulate the problem of spatiotemporal LST detection as a statistical sequential change detection problem. LST images are modeled as stochastic processes, with temperature changes reflected as changes in the parameters (i.e., mean) of the process. A generalized likelihood ratio test is used to detect these changes and estimate the exact time/space where they occur. To minimize processing time and memory requirements, we represent LST images by their reduced dimensionality using direct cosine transformation followed by principal component analysis. Statistical sequential analysis is used to provide a unified mathematical framework for the detection of both abrupt and gradual changes in LST observations of Bridgeton Missouri landfill over 17 years.

关键词： Remote sensing Principal component analysis Land surface temperature Earth Coal Gaussian noise change detection algorithms

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