EFFICIENT SEMANTIC SEGMENTATION METHOD WITH STRIP POOLING FOR VHR REMOTE SENSING IMAGES

In this paper, we address the problem of multi-class semantic segmentation of high resolution remote sensing images with a deep convolutional neutral network based model named Strip Pooling Network(SPNet). The objects in high resolution remote sensing images vary greatly in size. Moreover, these objects have different extensibility and directions, such as long narrow roads and wide grasslands. These bring big challenge for traditional square pooling kernels. If we want to capture the long-rang dependencies and enough contextual information, we need to use large pooling kernel which definitely increases computation greatly and incorporates interference information from irrelevant regions. SPNet introduces a new pooling strategy, called strip pooling which uses a long but narrow kernel, i.e., 1 x N or N x 1. It can solve the above problem while preventing information from irrelevant regions. Meanwhile, two paths in the strip pooling module focus on the horizontal and vertical spatial dimensions respectively, which compensate for the lack of one path captured in the narrow dimension of each other. Experimental results on public available Potsdam dataset demonstrate that SPNet obtains an overall accuracy of 89.1%, which outperforms other state-of-the-art methods.