Aerial image semantic segmentation using DCNN predicted distance maps

This paper addresses the challenge of learning spatial context for the semantic segmentation of high-resolution aerial images using Deep Convolutional Neural Networks (DCNNs). The proposed solution involves deriving a signed distance map for each semantic class from a ground truth label map and training a DCNN to predict this distance map instead of a score map for each class. Since the distance between a target pixel and its nearest object boundary measures how far the pixel penetrates an object, the distance maps encode spatial context, particularly spatial smoothness. Positive pixel values in the distance maps correspond to the correct class and negative values correspond to the incorrect class. A final label map is derived from the predicted distance maps by selecting the class with the maximum distance. Since neighboring pixels in the distance maps have similar values, the segmentation results are smoother than current approaches. The results are shown to be even better than performing post-processing using fully connected Conditional Random Fields (CRFs), a common approach to smoothing the segmentations produced DCNNs. Experimental results on the semantic labeling challenge dataset show the proposed approach outperforms most state-of-the-art methods. Our main contribution, though, is the novel idea of replacing the pixel-wise class score maps of DCNNs with distance maps. This is therefore orthogonal and complementary to other techniques employed by the state-of-the-art methods and could therefore be used to improve upon them.