Man-made object detection is important for object detection from remote sensing images.In this paper 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.
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