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Open AccessArticle

PolSAR Image Classification via Learned Superpixels and QCNN Integrating Color Features

1
College of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China
2
Key Laboratory of Aerocraft Tracking Telementering & Command and Communication, Chongqing University, Chongqing 400044, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(15), 1831; https://doi.org/10.3390/rs11151831
Received: 27 June 2019 / Revised: 25 July 2019 / Accepted: 1 August 2019 / Published: 6 August 2019
(This article belongs to the Section Remote Sensing Image Processing)
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Abstract

Polarimetric synthetic aperture radar (PolSAR) image classification plays an important role in various PolSAR image application. And many pixel-wise, region-based classification methods have been proposed for PolSAR images. However, most of the pixel-wise methods can not model local spatial relationship of pixels due to negative effects of speckle noise, and most of the region-based methods fail to figure out the regions with the similar polarimetric features. Considering that color features can provide good visual expression and perform well for image interpretation, in this work, based on the PolSAR pseudo-color image over Pauli decomposition, we propose a supervised PolSAR image classification approach combining learned superpixels and quaternion convolutional neural network (QCNN). First, the PolSAR RGB pseudo-color image is formed under Pauli decomposition. Second, we train QCNN with quaternion PolSAR data converted by RGB channels to extract deep color features and obtain pixel-wise classification map. QCNN treats color channels as a quaternion matrix excavating the relationship among the color channels effectively and avoiding information loss. Third, pixel affinity network (PAN) is utilized to generate the learned superpixels of PolSAR pseudo-color image. The learned superpixels allow the local information exploitation available in the presence of speckle noise. Finally, we fuse the pixel-wise classification result and superpixels to acquire the ultimate pixel-wise PolSAR image classification map. Experiments on three real PolSAR data sets show that the proposed approach can obtain 96.56%, 95.59%, and 92.55% accuracy for Flevoland, San Francisco and Oberpfaffenhofen data set, respectively. And compared with state-of-the-art PolSAR image classification methods, the proposed algorithm can obtained competitive classification results. View Full-Text
Keywords: polarimetric synthetic aperture radar (PolSAR); image classification; deep learning; superpixels; quaternion convolutional neural network (QCNN) polarimetric synthetic aperture radar (PolSAR); image classification; deep learning; superpixels; quaternion convolutional neural network (QCNN)
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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, X.; Xia, J.; Tan, X.; Zhou, X.; Wang, T. PolSAR Image Classification via Learned Superpixels and QCNN Integrating Color Features. Remote Sens. 2019, 11, 1831.

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