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Appl. Sci. 2017, 7(5), 447;

Dual-Branch Deep Convolution Neural Network for Polarimetric SAR Image Classification

School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
Department of Computer and Electronic Engineering, Wuzhou University, Wuzhou 543000, China
Cognitive Signal-Image and Control Processing Research Laboratory, School of Natural Sciences University of Stirling, Stirling FK9 4LA, UK
Space Mechatronic System Technology Laboratory, Department of Design, Manufacture and Engineering Management University of Strathclyde, Glasgow G1 1XJ, UK
Author to whom correspondence should be addressed.
Academic Editors: Carlos López-Martínez and Juan Manuel Lopez-Sanchez
Received: 9 March 2017 / Revised: 23 April 2017 / Accepted: 24 April 2017 / Published: 27 April 2017
(This article belongs to the Special Issue Polarimetric SAR Techniques and Applications)
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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. View Full-Text
Keywords: polarimetric SAR images; deep convolution neural network; dual-branch convolution neural network; land cover classification polarimetric SAR images; deep convolution neural network; dual-branch convolution neural network; land cover classification

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Gao, F.; Huang, T.; Wang, J.; Sun, J.; Hussain, A.; Yang, E. Dual-Branch Deep Convolution Neural Network for Polarimetric SAR Image Classification. Appl. Sci. 2017, 7, 447.

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