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Remote Sens. 2017, 9(5), 500; doi:10.3390/rs9050500

Learning Dual Multi-Scale Manifold Ranking for Semantic Segmentation of High-Resolution Images

1
School of Remote Sensing and Information Engineering, 129 Luoyu Road, Wuhan University, Wuhan 430079, China
2
Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China
3
School of Resource and Environmental Sciences, 129 Luoyu Road, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Academic Editors: Qi Wang, Nicolas H. Younan, Carlos López-Martínez and Prasad S. Thenkabail
Received: 9 March 2017 / Revised: 12 May 2017 / Accepted: 12 May 2017 / Published: 19 May 2017
(This article belongs to the Special Issue Learning to Understand Remote Sensing Images)
View Full-Text   |   Download PDF [42450 KB, uploaded 19 May 2017]   |  

Abstract

Semantic image segmentation has recently witnessed considerable progress by training deep convolutional neural networks (CNNs). The core issue of this technique is the limited capacity of CNNs to depict visual objects. Existing approaches tend to utilize approximate inference in a discrete domain or additional aides and do not have a global optimum guarantee. We propose the use of the multi-label manifold ranking (MR) method in solving the linear objective energy function in a continuous domain to delineate visual objects and solve these problems. We present a novel embedded single stream optimization method based on the MR model to avoid approximations without sacrificing expressive power. In addition, we propose a novel network, which we refer to as dual multi-scale manifold ranking (DMSMR) network, that combines the dilated, multi-scale strategies with the single stream MR optimization method in the deep learning architecture to further improve the performance. Experiments on high resolution images, including close-range and remote sensing datasets, demonstrate that the proposed approach can achieve competitive accuracy without additional aides in an end-to-end manner. View Full-Text
Keywords: semantic segmentation; deep convolutional neural networks; manifold ranking; single stream optimization; high resolution image semantic segmentation; deep convolutional neural networks; manifold ranking; single stream optimization; high resolution image
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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).

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Zhang, M.; Hu, X.; Zhao, L.; Lv, Y.; Luo, M.; Pang, S. Learning Dual Multi-Scale Manifold Ranking for Semantic Segmentation of High-Resolution Images. Remote Sens. 2017, 9, 500.

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