Semantic Segmentation of Remote Sensing Images Using Multiscale Decoding Network

Xiaoqin Zhang, Zhiheng Xiao, Dongyang Li, Mingyu Fan, Li Zhao

Abstract: In this letter, we propose a practical convolutional neural network architecture for semantic pixelwise segmentation of remote sensing images, named Multiscale Decoding Network. The proposed method is built on the success of fully convolutional networks (FCNs) and the transfer of pretrained networks. The decoding network of our architecture utilizes the combination of three paths, namely, unpooling path, transposed convolution path, and dilated convolution path, in the form of an inception module. The whole network is trained in the end-to-end manner and the parameters of the three paths are learned automatically. Since the proposed method transfers the feature of pretrained networks and has three simplified decoding paths with fewer parameters, it requires less training data and training time. Compared with the classical networks FCN, SegNet, and U-net, our network shows better performance on remote sensing images segmentation.

External IDs:dblp:journals/lgrs/ZhangXLFZ19