Next Article in Journal
Exploring Relationships among Tree-Ring Growth, Climate Variability, and Seasonal Leaf Activity on Varying Timescales and Spatial Resolutions
Next Article in Special Issue
Optimized Kernel Minimum Noise Fraction Transformation for Hyperspectral Image Classification
Previous Article in Journal
Recent Deceleration of the Ice Elevation Change of Ecology Glacier (King George Island, Antarctica)
Previous Article in Special Issue
Hypergraph Embedding for Spatial-Spectral Joint Feature Extraction in Hyperspectral Images
Article Menu
Issue 6 (June) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(6), 522;

Hourglass-ShapeNetwork Based Semantic Segmentation for High Resolution Aerial Imagery

ETRO Department, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
School of Electronic and Information Engineering, Beihang University, 37 Xueyuan Rd., Haidian District, Beijing 100191, China
Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China
Author to whom correspondence should be addressed.
Academic Editors: Qi Wang, Nicolas H. Younan, Carlos López-Martínez, Gonzalo Pajares Martinsanz and Prasad S. Thenkabail
Received: 5 April 2017 / Revised: 12 May 2017 / Accepted: 18 May 2017 / Published: 25 May 2017
(This article belongs to the Collection Learning to Understand Remote Sensing Images)
PDF [10872 KB, uploaded 25 May 2017]


A new convolution neural network (CNN) architecture for semantic segmentation of high resolution aerial imagery is proposed in this paper. The proposed architecture follows an hourglass-shaped network (HSN) design being structured into encoding and decoding stages. By taking advantage of recent advances in CNN designs, we use the composed inception module to replace common convolutional layers, providing the network with multi-scale receptive areas with rich context. Additionally, in order to reduce spatial ambiguities in the up-sampling stage, skip connections with residual units are also employed to feed forward encoding-stage information directly to the decoder. Moreover, overlap inference is employed to alleviate boundary effects occurring when high resolution images are inferred from small-sized patches. Finally, we also propose a post-processing method based on weighted belief propagation to visually enhance the classification results. Extensive experiments based on the Vaihingen and Potsdam datasets demonstrate that the proposed architectures outperform three reference state-of-the-art network designs both numerically and visually. View Full-Text
Keywords: semantic labeling; convolutional neural networks; remote sensing; deep learning; aerial images semantic labeling; convolutional neural networks; remote sensing; deep learning; aerial images

Graphical abstract

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).

Share & Cite This Article

MDPI and ACS Style

Liu, Y.; Minh Nguyen, D.; Deligiannis, N.; Ding, W.; Munteanu, A. Hourglass-ShapeNetwork Based Semantic Segmentation for High Resolution Aerial Imagery. Remote Sens. 2017, 9, 522.

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



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