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

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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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ECHAD: Embedding-Based change detection From Multivariate Time Series in Smart Grids

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IEEE ACCESS 2020年 8卷 156053-156066页

作者： Ceci, Michelangelo Corizzo, Roberto Japkowicz, Nathalie Mignone, Paolo Pio, Gianvito Univ Bari Aldo Moro Dept Comp Sci I-70125 Bari Italy Jozef Stefan Inst Dept Knowledge Technol Ljubljana 1000 Slovenia Natl Interuniv Consortium Informat CINI Big Data Lab I-00185 Rome Italy Amer Univ Dept Comp Sci Washington DC 20016 USA

Smart grids are power grids where clients may actively participate in energy production, storage and distribution. Smart grid management raises several challenges, including the possible changes and evolutions in terms of energy consumption and production, that must be taken into account in order to properly regulate the energy distribution. In this context, machine learning methods can be fruitfully adopted to support the analysis and to predict the behavior of smart grids, by exploiting the large amount of streaming data generated by sensor networks. In this article, we propose a novel change detection method, called ECHAD (Embedding-based change detection), that leverages embedding techniques, one-class learning, and a dynamic detection approach that incrementally updates the learned model to reflect the new data distribution. Our experiments show that ECHAD achieves optimal performances on synthetic data representing challenging scenarios. Moreover, a qualitative analysis of the results obtained on real data of a real power grid reveals the quality of the change detection of ECHAD. Specifically, a comparison with state-of-the-art approaches shows the ability of ECHAD in identifying additional relevant changes, not detected by competitors, avoiding false positive detections.

关键词： Smart grids Time series analysis Task analysis Feature extraction Data models change detection algorithms smart grids one-class learning neural networks embedding

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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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