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《Acta Geodaetica et Cartographica Sinica》 2018-06
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A Variational Approach for Automatic Man-made Object Detection from Remote Sensing Images

HU Xiangyun;GONG Xiaoya;ZHANG Mi;School of Remote Sensing and Information Engineering,Wuhan University;  
Man-made object detection is important for object detection from remote sensing images.Inthispaper we propose a variational approach for man-made object detection which formulates the man-made object detection problem as a problem of variational energy optimization.In this method,an image is firstly segmented into superpixels,and the saliency map by combining image features such as texture,color and gradient is computed.In second step,we construct an energy function with saliency,area,edge,texture and intensity variance constrains.The energy function is solved via variational method to obtain the foreground,which is the detected man-made objects.The proposed approach on several remote sensing images is evaluated and compared with the C-V model,MRF model and deep learning based semantic segmentation.Experimental results show that the proposed approach can effectively detect man-made objects on remote sensing images with low false alarm and false negatives rates.The comparison and analysis with deep learning based method are also presented.
【Fund】: 国家重点研发计划(2016YFB0501403);; 国家自然科学基金(41771363)~~
【CateGory Index】: TP751
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