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Open AccessArticle

A Hierarchical Fully Convolutional Network Integrated with Sparse and Low-Rank Subspace Representations for PolSAR Imagery Classification

by Yan Wang 1, Chu He 1,2,*, Xinlong Liu 1 and Mingsheng Liao 2,3
1
Electronic Information School, Wuhan University, Wuhan 430072, China
2
State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
3
Collaborative Innovation Center of Geospatial Technology, 129 Luoyu Road, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(2), 342; https://doi.org/10.3390/rs10020342
Received: 14 January 2018 / Revised: 10 February 2018 / Accepted: 13 February 2018 / Published: 23 February 2018
(This article belongs to the Special Issue Deep Learning for Remote Sensing)
Inspired by enormous success of fully convolutional network (FCN) in semantic segmentation, as well as the similarity between semantic segmentation and pixel-by-pixel polarimetric synthetic aperture radar (PolSAR) image classification, exploring how to effectively combine the unique polarimetric properties with FCN is a promising attempt at PolSAR image classification. Moreover, recent research shows that sparse and low-rank representations can convey valuable information for classification purposes. Therefore, this paper presents an effective PolSAR image classification scheme, which integrates deep spatial patterns learned automatically by FCN with sparse and low-rank subspace features: (1) a shallow subspace learning based on sparse and low-rank graph embedding is firstly introduced to capture the local and global structures of high-dimensional polarimetric data; (2) a pre-trained deep FCN-8s model is transferred to extract the nonlinear deep multi-scale spatial information of PolSAR image; and (3) the shallow sparse and low-rank subspace features are integrated to boost the discrimination of deep spatial features. Then, the integrated hierarchical subspace features are used for subsequent classification combined with a discriminative model. Extensive experiments on three pieces of real PolSAR data indicate that the proposed method can achieve competitive performance, particularly in the case where the available training samples are limited. View Full-Text
Keywords: fully convolutional network (FCN); polarimetric synthetic aperture radar (PolSAR); image classification; subspace learning; graph embedding (GE); sparse representation (SR); low-rank representation (LRR) fully convolutional network (FCN); polarimetric synthetic aperture radar (PolSAR); image classification; subspace learning; graph embedding (GE); sparse representation (SR); low-rank representation (LRR)
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MDPI and ACS Style

Wang, Y.; He, C.; Liu, X.; Liao, M. A Hierarchical Fully Convolutional Network Integrated with Sparse and Low-Rank Subspace Representations for PolSAR Imagery Classification. Remote Sens. 2018, 10, 342.

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