Learnable Gated Convolutional Neural Network for Semantic Segmentation in Remote-Sensing Images

• Main Authors: Shichen Guo, Qizhao Jin, Hongzhen Wang, Xuezhi Wang, Yangang Wang, Shiming Xiang
• Format: Article
• Language: English
• Published: MDPI AG 2019-08-01
• Series: Remote Sensing
• Subjects: semantic segmentation; CNN; deep learning; remote sensing; gate function; multiscale feature fusion
• Online Access: https://www.mdpi.com/2072-4292/11/16/1922

Semantic segmentation in high-resolution remote-sensing (RS) images is a fundamental task for RS-based urban understanding and planning. However, various types of artificial objects in urban areas make this task quite challenging. Recently, the use of Deep Convolutional Neural Networks (DCNNs) with multiscale information fusion has demonstrated great potential in enhancing performance. Technically, however, existing fusions are usually implemented by summing or concatenating feature maps in a straightforward way. Seldom do works consider the spatial importance for global-to-local context-information aggregation. This paper proposes a Learnable-Gated CNN (L-GCNN) to address this issue. Methodologically, the Taylor expression of the information-entropy function is first parameterized to design the gate function, which is employed to generate pixelwise weights for coarse-to-fine refinement in the L-GCNN. Accordingly, a Parameterized Gate Module (PGM) was designed to achieve this goal. Then, the single PGM and its densely connected extension were embedded into different levels of the encoder in the L-GCNN to help identify the discriminative feature maps at different scales. With the above designs, the L-GCNN is finally organized as a self-cascaded end-to-end architecture that is able to sequentially aggregate context information for fine segmentation. The proposed model was evaluated on two public challenging benchmarks, the ISPRS 2Dsemantic segmentation challenge Potsdam dataset and the Massachusetts building dataset. The experiment results demonstrate that the proposed method exhibited significant improvement compared with several related segmentation networks, including the FCN, SegNet, RefineNet, PSPNet, DeepLab and GSN.For example, on the Potsdam dataset, our method achieved a 93.65% <inline-formula> <math display="inline"> <semantics> <msub> <mi>F</mi> <mn>1</mn> </msub> </semantics> </math> </inline-formula> score and 88.06% <inline-formula> <math display="inline"> <semantics> <mrow> <mi>I</mi> <mi>o</mi> <mi>U</mi> </mrow> </semantics> </math> </inline-formula> score for the segmentation of tiny cars in high-resolution RS images. As a conclusion, the proposed model showed potential for object segmentation from the RS images of buildings, impervious surfaces, low vegetation, trees and cars in urban settings, which largely varies in size and have confusing appearances.