Next Article in Journal
Better Estimated IEM Input Parameters Using Random Fractal Geometry Applied on Multi-Frequency SAR Data
Next Article in Special Issue
Hyperspectral Dimensionality Reduction by Tensor Sparse and Low-Rank Graph-Based Discriminant Analysis
Previous Article in Journal
Using Remote-Sensing Environmental and Fishery Data to Map Potential Yellowfin Tuna Habitats in the Tropical Pacific Ocean
Previous Article in Special Issue
Image Registration and Fusion of Visible and Infrared Integrated Camera for Medium-Altitude Unmanned Aerial Vehicle Remote Sensing
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(5), 446; doi:10.3390/rs9050446

Gated Convolutional Neural Network for Semantic Segmentation in High-Resolution Images

1
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
2
University of Chinese Academy of Sciences, Beijing 101408, China
3
Alibaba Group, Beijing 100102, China
*
Author to whom correspondence should be addressed.
Academic Editors: Qi Wang, Nicolas H. Younan, Carlos López-Martínez, Xiaofeng Li and Prasad S. Thenkabail
Received: 2 April 2017 / Revised: 27 April 2017 / Accepted: 1 May 2017 / Published: 5 May 2017
(This article belongs to the Collection Learning to Understand Remote Sensing Images)
View Full-Text   |   Download PDF [2391 KB, uploaded 12 May 2017]   |  

Abstract

Semantic segmentation is a fundamental task in remote sensing image processing. The large appearance variations of ground objects make this task quite challenging. Recently, deep convolutional neural networks (DCNNs) have shown outstanding performance in this task. A common strategy of these methods (e.g., SegNet) for performance improvement is to combine the feature maps learned at different DCNN layers. However, such a combination is usually implemented via feature map summation or concatenation, indicating that the features are considered indiscriminately. In fact, features at different positions contribute differently to the final performance. It is advantageous to automatically select adaptive features when merging different-layer feature maps. To achieve this goal, we propose a gated convolutional neural network to fulfill this task. Specifically, we explore the relationship between the information entropy of the feature maps and the label-error map, and then a gate mechanism is embedded to integrate the feature maps more effectively. The gate is implemented by the entropy maps, which are generated to assign adaptive weights to different feature maps as their relative importance. Generally, the entropy maps, i.e., the gates, guide the network to focus on the highly-uncertain pixels, where detailed information from lower layers is required to improve the separability of these pixels. The selected features are finally combined to feed into the classifier layer, which predicts the semantic label of each pixel. The proposed method achieves competitive segmentation accuracy on the public ISPRS 2D Semantic Labeling benchmark, which is challenging for segmentation by only using the RGB images. View Full-Text
Keywords: semantic segmentation; CNN; deep learning; ISPRS; remote sensing; gate semantic segmentation; CNN; deep learning; ISPRS; remote sensing; gate
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Wang, H.; Wang, Y.; Zhang, Q.; Xiang, S.; Pan, C. Gated Convolutional Neural Network for Semantic Segmentation in High-Resolution Images. Remote Sens. 2017, 9, 446.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top