Open AccessChange detection based on dimension reduction SLIC and image matching for remote sensing images
Author(s) -
Zhenliang Chang,
Xiaogang Yang,
Ruitao Lü,
Hao Zhuang,
Pan Huang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2078/1/012040
Subject(s) - change detection , artificial intelligence , computer science , robustness (evolution) , computer vision , image segmentation , segmentation , pixel , pattern recognition (psychology) , image (mathematics) , homogeneity (statistics) , remote sensing , biochemistry , chemistry , machine learning , geology , gene
The detection accuracy of traditional change detection algorithms is seriously affected by the low accuracy and high rate of omission, the radiometric correction accuracy, and the classification threshold for difference image. A change detection method based on image segmentation and image matching was proposed for remote sensing images. In this method, super-pixel-based dimension reduction SLIC image segmentation algorithm and SURF algorithms were used. The homogeneous region was used as the segmentation standard, and the homogeneity method was proposed to suppress the impact of inconsistent image segmentation on the change detection results. The experimental results show that this method improves the accuracy of remote sensing image change detection, has good robustness to the problem of redundant data, significantly reduces the error detection rate of image change detection, and can effectively accelerate the speed of change detection.