关键词: 高分辨率遥感图像, 语义分割, 深度学习, 多尺度语义分割网络（MSSNet）

Abstract:

The semantic segmentation of very-high-resolution remote sensing images have wide application value in land planning, geographic monitoring and smart cities. But at present study, there are the problems of inaccurate segmentation of similar objects and fine-structured objects. In order to solve these problems, a new Multiscale Semantic Segmentation Network（MSSNet） is proposed. It consists of encoding layer, decoding layer and output layer. For the problem of similar objects, ResNet101 is used in the encoding layer to fully extract object features and residual block is added in the decoder of the decoding layer to improve the classification ability based on pixel points. For problem of fine-structured objects segmentation, the atrous spatial pyramid pooling is added in the decoder to extract multi-scale object features. So that objects of different scales can be accurately segmented. To enhance the ability of semantic segmentation, the output layer combines the output of multiple decoders. This provides more information for the final forecast. Experiments on two open datasets, Vaihingen and Potsdam, have achieved overall accuracy of 87% and 87.3% respectively, which surpasses most published methods. The results show that MSSNet can accurately segment similar objects and fine-structured objects. And it has the advantages of simple training process and easy to use. It is very suitable for semantic segmentation of very-high-resolution remote sensing images.

Key words: very-high-resolution remote sensing image, semantic segmentation, deep learning, Multiscale Semantic Segment Network（MSSNet）