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Open AccessArticle

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

1
Computer Network Information Center, Chinese Academy of Sciences, 4 Zhongguancun Nansi Street, Beijing 100190, China
2
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95# Zhongguancun East Road, Beijing 100190, China
3
School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(16), 1922; https://doi.org/10.3390/rs11161922
Received: 29 July 2019 / Revised: 13 August 2019 / Accepted: 15 August 2019 / Published: 17 August 2019
(This article belongs to the Section Remote Sensing Image Processing)
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% F 1 score and 88.06% I o U 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. View Full-Text
Keywords: semantic segmentation; CNN; deep learning; remote sensing; gate function; multiscale feature fusion semantic segmentation; CNN; deep learning; remote sensing; gate function; multiscale feature fusion
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MDPI and ACS Style

Guo, S.; Jin, Q.; Wang, H.; Wang, X.; Wang, Y.; Xiang, S. Learnable Gated Convolutional Neural Network for Semantic Segmentation in Remote-Sensing Images. Remote Sens. 2019, 11, 1922. https://doi.org/10.3390/rs11161922

AMA Style

Guo S, Jin Q, Wang H, Wang X, Wang Y, Xiang S. Learnable Gated Convolutional Neural Network for Semantic Segmentation in Remote-Sensing Images. Remote Sensing. 2019; 11(16):1922. https://doi.org/10.3390/rs11161922

Chicago/Turabian Style

Guo, Shichen; Jin, Qizhao; Wang, Hongzhen; Wang, Xuezhi; Wang, Yangang; Xiang, Shiming. 2019. "Learnable Gated Convolutional Neural Network for Semantic Segmentation in Remote-Sensing Images" Remote Sens. 11, no. 16: 1922. https://doi.org/10.3390/rs11161922

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