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Journal Publication

Hourglass-ShapeNetwork Based Semantic Segmentation for High Resolution Aerial Imagery

This publication appears in: Remote Sensing

Authors: D. Nguyen, N. Deligiannis, W. Ding and A. Munteanu

Volume: 9

Issue: 6

Number of Pages: 24

Publication Date: May. 2017


Abstract:

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.

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Mr. Duc Nguyen

+32 (0)02 629 168

mdnguyen@etrovub.be

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Prof. Dr. Ir. Nikolaos Deligiannis

+32 (0)02 629 168

ndeligia@etrovub.be

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Prof. Dr. Ir. Adrian Munteanu

+32 (0)02 629 168

acmuntea@etrovub.be

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