MSINet: Mining scale information from digital surface models for semantic segmentation of aerial images

Abstract

Compared with other kinds of images, aerial images have more obvious object scale distinction and larger resolution, which results in that the whole scale information of aerial images can hardly be explored. To address this difficulty, we develop a novel network based on the digital surface models (DSMs) of aerial images in this paper. The proposed network termed MSINet can efficiently mine scale information through the DSMs from two aspects. Firstly, we propose an interpolation pyramid algorithm to encode the scale information from the DSMs and hence provide a scale prior information to the normal segmentation network. The interpolation pyramid algorithm implements interpolation operations with different scales on the DSMs and detects the pixel value change after the interpolation operations. Objects with different scales will express diverse changes, which provides useful information to encode their scale information. Secondly, aiming to address the problem that the DSMs contain a large amount of noise in the boundary part, a spatial information enhancement module and a mutual-guidance module are developed in this paper. These two modules can fix the misleading guidance information caused by the noise in the boundary part of the DSMs and hence achieve more accurate scale information inserting. The extensive experimental results prove that our methods can outperform other competitors in terms of qualitative and quantitative performance.