- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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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 - Unsupervised Multi-Class Change Detection in Bitemporal Multispectral Images Using Band Expansion
摘要： This paper focuses on solving the multi-class change detection problem in bitemporal multispectral remote sensing images. In that case, information that represented in a small number (e.g., two) of the original bands may be insufficient for the accurate identification of a few of multi-class changes. In particular, this problem becomes more difficult in unsupervised change detection cases when ground reference data is not available. In this paper, a solution is proposed by using the potential information represented in expanded features that constructed from the original spectral bands. Experimental results obtained on a real bitemporal remote sensing data set confirm the effectiveness of the proposed approach.
关键词： change vector analysis,feature expansion,remote sensing,Change detection,multi-class changes
更新于2025-09-10 09:29:36
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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 - An Unsupervised Change Detection Method for Lidar Data in Forest Areas Based on Change Vector Analysis in the Polar Domain
摘要： This paper presents a Change Detection method for bitemporal Light Detection And Ranging (LiDAR) data based on Change Vector Analysis in the polar domain. The method first extracts a suite of LiDAR metrics from the two LiDAR point clouds using a 2-D grid based approach. Second, it transforms the change in these metrics into a polar representation to examine variations in terms of magnitude and direction. The analysis of the magnitude discriminates between small magnitude changes or unchanged areas and areas affected by large disturbances related to forest removal. The analysis of the direction of change allows us to identify dominant directions to discriminate between the various types of forest change. The method has been tested on a multitemporal dataset acquired in a high productivity evergreen conifer forest in British Columbia, Canada. Experimental results indicated that the method effectively discriminates between the different types of forest change trought the analysis of the change direction.
关键词： Multitemporal,Change Vector Analysis,LiDAR,Forestry,Change Detection
更新于2025-09-10 09:29:36
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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
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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 - Fuzzy Fusion of Change Vector Analysis and Spectral Angle Mapper for Hyperspectral Change Detection
摘要： Change Vector Analysis (CVA) is one of the most widely used approaches for change detection in multispectral and hyperspectral images. Although, in CVA, the spectral change vector (CV) comprises the angle as well as the magnitude of the change, typically only the magnitude measure is used as change criterion. On the other hand, the spectral angle mapper (SAM) uses only the angle measure as criterion for change detection. It is envisaged that combining the angle and magnitude for change detection (i.e. combining SAM and magnitude CVA) can improve the change detection performance, yet only a limited number of approaches have been proposed in the literature so far. This paper presents a novel fuzzy inference combination strategy that combines the angle and magnitude distances, referred to as Fuzzy CVA (FuzCVA), and is shown that the proposed approach can provide improved change detection performance by effectively combining magnitude and angle measures.
关键词： Hyperspectral Imaging,change detection,spectral angle mapper,fuzzy inference,change vector analysis
更新于2025-09-04 15:30:14