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Open AccessArticle

A Dual-Path and Lightweight Convolutional Neural Network for High-Resolution Aerial Image Segmentation

by Gang Zhang 1,2, Tao Lei 1,*, Yi Cui 1 and Ping Jiang 1
1
Institute of Optics and Electronics, Chinese Academy of Sciences, P.O. Box 350, No.1 Guangdian Avenue, Chengdu 610209, China
2
University of Chinese Academy of Sciences, No. 19 (A) Yuquan Road, Beijing 100049, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(12), 582; https://doi.org/10.3390/ijgi8120582
Received: 7 October 2019 / Revised: 5 December 2019 / Accepted: 9 December 2019 / Published: 12 December 2019
Semantic segmentation on high-resolution aerial images plays a significant role in many remote sensing applications. Although the Deep Convolutional Neural Network (DCNN) has shown great performance in this task, it still faces the following two challenges: intra-class heterogeneity and inter-class homogeneity. To overcome these two problems, a novel dual-path DCNN, which contains a spatial path and an edge path, is proposed for high-resolution aerial image segmentation. The spatial path, which combines the multi-level and global context features to encode the local and global information, is used to address the intra-class heterogeneity challenge. For inter-class homogeneity problem, a Holistically-nested Edge Detection (HED)-like edge path is employed to detect the semantic boundaries for the guidance of feature learning. Furthermore, we improve the computational efficiency of the network by employing the backbone of MobileNetV2. We enhance the performance of MobileNetV2 with two modifications: (1) replacing the standard convolution in the last four Bottleneck Residual Blocks (BRBs) with atrous convolution; and (2) removing the convolution stride of 2 in the first layer of BRBs 4 and 6. Experimental results on the ISPRS Vaihingen and Potsdam 2D labeling dataset show that the proposed DCNN achieved real-time inference speed on a single GPU card with better performance, compared with the state-of-the-art baselines. View Full-Text
Keywords: remote sensing; convolutional neural networks; high-resolution aerial images; semantic segmentation; semantic boundaries; lightweight network remote sensing; convolutional neural networks; high-resolution aerial images; semantic segmentation; semantic boundaries; lightweight network
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Zhang, G.; Lei, T.; Cui, Y.; Jiang, P. A Dual-Path and Lightweight Convolutional Neural Network for High-Resolution Aerial Image Segmentation. ISPRS Int. J. Geo-Inf. 2019, 8, 582.

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