Automatic Semantic Segmentation for Change Detection in Remote Sensing Images
Change detection (CD) mainly focuses on the extraction of change information from multispectral remote sensing images of the same geographical location for environmental monitoring, natural disaster evaluation, urban studies, and deforestation monitoring. While capturing the Landsat imagery, there may occur data missing issues such as occlusion of cloud, camera sensor, and aperture artifacts. The existing machine learning approaches do not provide significant results. This paper proposes a DeepLab Dilated convolutional neural network (DL-DCNN) for semantic segmentation with the goal to occur the change map for earth observation applications. Experimental results reveal that the accuracy of the proposed change detection results provides improved results as compared with the existing algorithms and maps the semantic objects within the predefined class as change or no change.
KeywordsChange detection Remote sensing Multispectral Deep learning
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