- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Indicator-Kriging-Integrated Evidence Theory for Unsupervised Change Detection in Remotely Sensed Imagery
摘要： This study proposes a novel approach based on indicator kriging and Dempster–Shafer (DS) theory for unsupervised change detection (CD) in remote sensing images (DSK). Indicator kriging is integrated to the standard DS theory. A feature set with four difference images (DIs) providing complementary change information is initially generated. Subsequently, the mass functions for each DI are determined automatically using fuzzy logic, the four pieces of DI evidence are combined by DS theory, and a preliminary CD map is achieved. The preliminary CD map is then divided into three parts adaptively—weakly con?icting part of no change, weakly con?icting part of change, and strongly con?icting part—by calculating the evidence con?ict degree for each pixel. Finally, the pixels in the weakly con?icting parts, which have little or no con?ict, are labeled as the current class, and the pixels in the strongly con?icting part that contains misclassi?ed pixels are reclassi?ed based on indicator kriging. DSK combines the advantages of different DI features and solves the con?icting situations to a large extent. The main contributions of this study include the following: 1) introducing indicator kriging into CD to manage con?ict information during DS fusion and 2) presenting a scheme for producing DI set with complementary change information, developing a novel DSK fusion model for information fusion, and de?ning the proposed CD framework. Experimental results verify that the proposed DSK is robust and effective for CD.
关键词： unsupervised change detection (CD),remote sensing,Con?ict management,indicator kriging,Dempster–Shafer (DS) theory
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Pr-Based Sar Reconstruction Autofocus Algorithm for Persistent Surveillance Change Detection
摘要： Random phase noises arising from frequency jitter of transmit signal and atmospheric turbulence result in corrupted synthetic aperture radar (SAR) imagery, which in turn degrades change detection (CD) performance. In this paper, a phase retrieval (PR) based SAR reconstruction autofocus framework by exploiting the hidden convexity is proposed with the goal of achieving reliable persistent surveillance CD. Firstly the original non-convex quartic SAR reconstruction is reformulated as a convex quadratic program. Under the minimum phase assumption, the auto-correlation retrieval-Kolmogorov factorization (CoRK) algorithm is then utilized to optimally and efficiently retrieve the underlying SAR reflectivity. The devised scheme possesses effective capabilities of phase noise mitigation, thus has a superior CD performance. Experimental results are provided to verify the effectiveness of the proposed method.
关键词： Synthetic aperture radar (SAR),hidden convexity,change detection (CD),phase retrieval (PR),random phase noises
更新于2025-09-23 15:21:21
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[IEEE 2019 IEEE International Conference on Electron Devices and Solid-State Circuits (EDSSC) - Xi'an, China (2019.6.12-2019.6.14)] 2019 IEEE International Conference on Electron Devices and Solid-State Circuits (EDSSC) - A Non-Ohmic Normally-off GaN RB-MISHEMT Featuring a Gate-Controlled Schottky Tunnel Junction
摘要： Due to the limited number of spectral channels in multispectral remote sensing images, change information, especially the multiclass changes, may be insuf?ciently represented, resulting in inaccurate detection of changes. In this paper, we propose to use unsupervised band expansion techniques to generate arti?cial spectral and spatial bands to enhance the change representation and discrimination for change detection (CD) from multispectral images. In particular, in the proposed approach, two simple nonlinear functions, i.e., multiplication and division, are applied for spectral expansion. Multiscale morphological reconstruction is used to extend the band spatial information. The expanded band sets are then used and validated in three popular unsupervised CD techniques for solving a multiclass CD problem. Experimental results obtained on three real bitemporal multispectral remote sensing datasets con?rm the effectiveness of the proposed approach.
关键词： Change detection (CD),remote sensing,nonlinear band expansion,change vector analysis,multitemporal analysis,multispectral images,dimensionality expansion
更新于2025-09-11 14:15:04
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - ROBUST PCANet for Hyperspectral Image Change Detection
摘要： Deep learning is an effective tool for handling high-dimensional data and modeling nonlinearity, which can tackle the hyper-spectral data well. Usually deep learning methods need a large number of training samples. However, there is no labeled data for training in change detection (CD). Considering these, this paper develops an unsupervised Robust PCA network (RPCANet) for hyperspectral image CD task. The main contributions of this work are twofold: 1) An unsupervised convolutional neural networks named RPCANet is proposed to handle the hyperspectral image CD; 2) An effective CD framework using the RPCANet and change vector analysis (CVA) is designed to achieve better CD performance with more powerful features. Experimental results on real hyperspectral datasets demonstrate the effectiveness of the proposed method.
关键词： change detection (CD),Robust PCA network (RPCANet),Hyperspectral image,change vector analysis (CVA)
更新于2025-09-09 09:28:46