Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/25527
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Dual-branch deep convolution neural network for polarimetric SAR image classification
Authors: Gao, Fei
Huang, Teng
Wang, Jun
Sun, Jinping
Hussain, Amir
Yang, Erfu
Keywords: polarimetric SAR images
deep convolution neural network
dual-branch convolution neural network
land cover classification
Issue Date: 27-Apr-2017
Citation: Gao F, Huang T, Wang J, Sun J, Hussain A & Yang E (2017) Dual-branch deep convolution neural network for polarimetric SAR image classification, Applied Sciences, 7 (5), Art. No.: 447.
Abstract: The deep convolution neural network (CNN), which has prominent advantages in feature learning, can learn and extract features from data automatically. Existing polarimetric synthetic aperture radar (PolSAR) image classification methods based on the CNN only consider the polarization information of the image, instead of incorporating the image’s spatial information. In this paper, a novel method based on a dual-branch deep convolution neural network (Dual-CNN) is proposed to realize the classification of PolSAR images. The proposed method is built on two deep CNNs: one is used to extract the polarization features from the 6-channel real matrix (6Ch) which is derived from the complex coherency matrix. The other is utilized to extract the spatial features of a Pauli RGB (Red Green Blue) image. These extracted features are first combined into a fully connected layer sharing the polarization and spatial property. Then, the Softmax classifier is employed to classify these features. The experiments are conducted on the Airborne Synthetic Aperture Radar (AIRSAR) data of Flevoland and the results show that the classification accuracy on 14 types of land cover is up to 98.56%. Such results are promising in comparison with other state-of-the-art methods.
DOI Link: http://dx.doi.org/10.3390/app7050447
Rights: © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

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