MSNet: Multifunctional Feature-Sharing Network for Land-Cover Segmentation

Abstract

In recent years, the resolution of remote sensing images, especially aerial images, has become higher and higher, and the spans of time and space have become larger and larger. The phenomenon in which one class of objects can produce several kinds of spectra may lead to more errors in detection methods that are based on spectra. For different convolution methods, downsampling can provide some advanced information, which will lead to rough detail extraction; too deep of a network will greatly increase the complexity and calculation time of a model. To solve these problems, a multifunctional feature extraction model called MSNet (multifunctional feature-sharing network) is proposed, which is improved on two levels: depth feature extraction and feature fusion. Firstly, a residual shuffle reorganization branch is proposed; secondly, linear index upsampling with different levels is proposed; finally, the proposed edge feature attention module allows the recovery of detailed features. The combination of the edge feature attention module and linear index upsampling can not only provide benefits in learning detailed information, but can also ensure the accuracy of deep feature extraction. The experiments showed that MSNet achieved 81.33% MIoU on the Landover dataset.