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Remote Sensing for Natural Resources    2021, Vol. 33 Issue (3) : 89-96     DOI: 10.6046/zrzyyg.2020312
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Change detection of remote sensing images based on the fusion of co-saliency difference images
WANG Yiuzhu1(), HUANG Liang1,2(), CHEN Pengdi1, LI Wenguo1, YU Xiaona3
1. Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China
2. Surveying and Mapping Geo-Informatics Technology Research Center on Plateau Mountains of Yunnan Higher Education, Kunming 650093, China
3. Kunming Vocational and Technical College of Industry, Kunming 650302, China
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Abstract  

Owing to the complex surface features in the high spatial resolution (HR) remote sensing images, traditional change detection methods suffer the shortcoming of a high omission rate. Given this, this paper proposed a change detection method based on multi-temporal remote sensing images based on the fusion of co-saliency difference images. In this study, three groups of dual-temporal HR remote sensing images were selected to carry out the experiment according to the following steps. First, develop difference images based on the dual-temporal HR remote sensing images using the methods of change vector analysis (CVA) and spectral gradient difference (SGD). Then obtain a co-saliency map of two difference images using the cluster-based co-saliency detection. Finally, obtain the co-saliency difference map by fusing two co-saliency maps, and then conduct threshold segmentation and closing operation of the co-saliency difference map using the OTSU method. In this way, the final change map was obtained. As indicated by the experiment results, this method is superior to traditional methods in terms of overall accuracy (OA), Kappa coefficient, and F-measure accuracy and thus is highly reliable and accurate.
Keywords change vector analysis      spectral gradient difference      co-saliency detection      change detection      remote sensing image     
ZTFLH:  TP79  
Corresponding Authors: HUANG Liang     E-mail: mmc55730924@163.com;kmhuangliang@163.com
Issue Date: 24 September 2021
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Yiuzhu WANG
Liang HUANG
Pengdi CHEN
Wenguo LI
Xiaona YU
Cite this article:   
Yiuzhu WANG,Liang HUANG,Pengdi CHEN, et al. Change detection of remote sensing images based on the fusion of co-saliency difference images[J]. Remote Sensing for Natural Resources, 2021, 33(3): 89-96.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2020312     OR     https://www.gtzyyg.com/EN/Y2021/V33/I3/89
Fig.1  Flow chart of the method
Fig.2  The first group of image change detection results
方法 FA MA OA Kappa F-measure
CVA-OTSU 26.80 24.16 73.43 8.83 25.10
SGD-OTSU 9.16 23.50 89.62 28.43 48.70
CWNN 13.22 87.73 80.40 0.19 8.70
FLICM 8.65 94.56 84.00 0.59 5.50
PCA-Kmeans 16.05 34.37 82.38 23.19 31.90
本文方法 4.09 26.18 94.02 64.59 65.00
Tab.1  Accuracy evaluation of the first group image(%)
Fig.3  The second group of image change detection results
方法 FA MA OA Kappa F-measure
CVA-OTSU 12.04 13.60 87.94 5.78 14.40
SGD-OTSU 19.10 57.15 80.22 1.96 5.00
CWNN 4.92 38.49 94.48 26.42 22.10
FLICM 5.34 19.16 94.41 32.12 25.90
PCA-Kmeans 9.36 5.56 90.71 13.38 19.20
本文方法 0.59 22.59 99.01 73.19 71.80
Tab.2  Accuracy evaluation of the second group image(%)
Fig.4  The third group of image change detection results
方法 FA MA OA Kappa F-measure
CVA-OTSU 16.64 6.90 83.62 6.07 16.50
SGD-OTSU 24.73 66.11 74.17 1.05 4.50
CWNN 17.96 17.73 82.05 15.59 13.80
FLICM 5.75 36.57 93.44 31.18 27.10
PCA-Kmeans 14.57 3.15 85.73 11.60 19.00
本文方法 1.35 33.64 97.80 60.36 59.10
Tab.3  Accuracy evaluation of the third group image(%)
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