Abstract
Information extraction from multi-sensor remote sensing images has increasingly attracted attention with the development of remote sensing sensors. In this study, a supervised change detection method, based on the deep Siamese convolutional network with hybrid convolutional feature extraction module (OB-DSCNH), has been proposed using multi-sensor images. The proposed architecture, which is based on dilated convolution, can extract the deep change features effectively, and the character of “network in network” increases the depth and width of the network while keeping the computational budget constant. The change decision model is utilized to detect changes through the difference of extracted features. Finally, a change detection map is obtained via an uncertainty analysis, which combines the multi-resolution segmentation, with the output from the Siamese network. To validate the effectiveness of the proposed approach, we conducted experiments on multispectral images collected by the ZY-3 and GF-2 satellites. Experimental results demonstrate that our proposed method achieves comparable and better performance than mainstream methods in multi-sensor images change detection.
Highlights
The detection of changes on the surface of the earth has become increasingly important for monitoring the local, regional, and global environment [1]
CD based on the deep Siamese mRuemltotie-Ssecnas.l2e01c9,o1n1,vx oFOluR tPiEoEnR aRElVnIEeWtwork (DSMS-CN) [36], the deep convolutional n11eoufr1a9 l network (DCNN), a3n.1d
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Summary
The detection of changes on the surface of the earth has become increasingly important for monitoring the local, regional, and global environment [1] It has been studied in a number of applications, including land use investigation [2,3], disaster evaluation [4], ecological environment monitoring, and geographic data update [5]. Object-based methods are often utilized in a change detection task, as pixel-based change detection methods may generate the high commission and omission errors, due to high within class variation [10] In this regard, the object-oriented technique has recently attracted considerable attention when handling high spatial resolution images [13,14,15]. These works are mainly developed for change detection using single sensor images, which have similar data properties
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