Unsupervised Change Detection of Multispectral Imagery Using Multi Level Fuzzy Based Deep Representation

Change detection (CD) is an important application of remote sensing, which provides timely change information about large-scale Earth surface. With the emergence of hyperspectral imagery, CD technology has been greatly promoted, as hyperspectral data with high spectral resolution are capable of detecting finer changes than using the traditional multispectral imagery. Nevertheless, the high dimension of hyperspectral data makes it difficult to implement traditional CD algorithms. Besides, endmember abundance information at subpixel level is often not fully utilized. In order to better handle high dimension problem and explore abundance information, this paper presents a General End-to-end Two-dimensional CNN (GETNET) framework for hyperspectral image change detection (HSI-CD). The main contributions of this work are threefold: 1) Mixed-affinity matrix that integrates subpixel representation is introduced to mine more cross-channel gradient features and fuse multi-source information; 2) 2-D CNN is designed to learn the discriminative features effectively from multi-source data at a higher level and enhance the generalization ability of the proposed CD algorithm; 3) A new HSI-CD data set is designed for objective comparison of different methods. Experimental results on real hyperspectral data sets demonstrate the proposed method outperforms most of the state-of-the-arts.

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GETNET: A General End-to-End 2-D CNN Framework for Hyperspectral Image Change Detection

Wang

Yuan

et al. 2019

IEEE Trans. Geosci. Remote Sensing

452

226

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An Integrated Change Detection Method Based on Spectral Unmixing and the CNN for Hyperspectral Imagery

2022

Remote Sensing

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Hyperspectral remote sensing image (HSI) include rich spectral information that can be very beneficial for change detection (CD) technology. Due to the existence of many mixed pixels, pixel-wise approaches can lead to considerable errors in the resulting CD map. The spectral unmixing (SU) method is a potential solution to this problem, as it decomposes mixed pixels into a set of fractions of land cover. Subsequently, the CD map is created by comparing the abundance images. However, based only on the abundance images created through the SU method, they are unable to effectively provide detailed change information. Meanwhile, the features of change information cannot be sufficiently extracted by the traditional sub-pixel CD framework, which leads to a poor CD result. To address these problems, this paper presents an integrated CD method based on multi-endmember spectral unmixing, joint matrix and CNN (MSUJMC) for HSI. Three main steps are considered to accomplish this task. First, considering the endmember spectral variability, more reliable endmember abundance information is obtained by multi-endmember spectral unmixing (MSU). Second, the original image features are incorporated with the abundance images using a joint matrix (JM) algorithm to provide more temporal and spatial land cover change information characteristics. Third, to efficiently extract the change features and to better handle the fused multi-source information, the convolutional neural network (CNN) is introduced to realize a high-accuracy CD result. The proposed method has been verified on simulated and real multitemporal HSI datasets, which provide multiple changes. Experimental results verify the effectiveness of the proposed approach.

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Unsupervised Change Detection of Multispectral Imagery Using Multi Level Fuzzy Based Deep Representation

Cited by 2 publications

References 12 publications

GETNET: A General End-to-End 2-D CNN Framework for Hyperspectral Image Change Detection

GETNET: A General End-to-End 2-D CNN Framework for Hyperspectral Image Change Detection

An Integrated Change Detection Method Based on Spectral Unmixing and the CNN for Hyperspectral Imagery

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