Pointnet: Learning Point Representation for High-Resolution Remote Sensing Imagery Land-Cover Classification

Abstract

With the development of remote sensing, a large amount of high-spatial-resolution (HSR) images is available, which makes refined land-cover mapping possible. However, the details of ground objects in HSR images are complex, especially in edges, therefore brings new challenges in land-cover classification. Existing deep learning method views it as a semantic segmentation task based on the fully convolutional networks (FCN), taking no account of complex details recognition. In this paper, we tackle this problem by proposing a point representation network (PointNet) for HSR land-cover classification Specifically, the uncertain point selection is designed for finding the most uncertain details at the end of ResNet encoder. According to these points, the coarse and fine features in the encoder are fused, followed by a multilayer perceptron (MLP). Different from convolutional sampling, the MLP focuses on the recognition of uncertain points, which modifies the network optimization during training. During the prediction, the coarse features are successively upsampled with the points refining, improving the performance on land-cover details recognition. Experimental results on a Nanjing land-cover dataset demonstrate that the PointNet outperforms the state-of-the-art methods.