Multispectral Image Change Detection Based on Single-band Slow Feature Analysis
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He, Youxi
Jia, Zhenhong
Yang, Jie
Kasabov, Nikola K.
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MDPI AG
Abstract
Due to differences in external imaging conditions, multispectral images taken at different periods are subject to radiation differences, which severely affect the detection accuracy. To solve this problem, a modified algorithm based on slow feature analysis is proposed for multispectral image change detection. First, single-band slow feature analysis is performed to process bitemporal multispectral images band by band. In this way, the differences between unchanged pixels in each pair of single-band images can be sufficiently suppressed to obtain multiple feature-difference images containing real change information. Then, the feature-difference images of each band are fused into a grayscale distance image using the Euclidean distance. After Gaussian filtering of the grayscale distance image, false detection points can be further reduced. Finally, the k-means clustering method is performed on the filtered grayscale distance image to obtain the binary change map. Experiments reveal that our proposed algorithm is less affected by radiation differences and has obvious advantages in time complexity and detection accuracy.
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4013 Geomatic Engineering, 40 Engineering, Biomedical Imaging, 0203 Classical Physics, 0406 Physical Geography and Environmental Geoscience, 0909 Geomatic Engineering, 3701 Atmospheric sciences, 3709 Physical geography and environmental geoscience, 4013 Geomatic engineering, change detection, multispectral remote sensing image, slow feature analysis
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Remote Sensing, ISSN: 2072-4292 (Print); 2072-4292 (Online), MDPI AG, 13(15), 2969-2969. doi: 10.3390/rs13152969
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Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. Open access
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Except where otherwise noted, this item's license is described as Creative Commons Attribution (CC BY) license