期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 59, 期 12, 页码 10179-10196出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2021.3053013
关键词
Feature extraction; Synthetic aperture radar; Covariance matrices; Neural networks; Measurement; Euclidean distance; Convolution; Convolutional neural network (CNN); deep learning; image classification; polarimetric synthetic aperture radar (PolSAR)
类别
资金
- Natural Science Foundation of China [61772390, 61871312, 61901358]
- Civil Space Thirteen Five Years Pre-Research Project [D040114]
- Natural Science Basic Research Plan in Shaanxi Province of China [2019JZ-14]
This paper proposes a PolSAR classification method using complex-valued distance comparisons, utilizing a triplet CV network for feature learning and coupling it with CV K nearest neighbor method. Experimental results demonstrate that this approach can effectively handle PolSAR data and achieve comparable performance in PolSAR image classification.
Recently, convolutional neural network (CNN) has proved itself as a successful deep model and has been successfully utilized in polarimetric synthetic aperture radar (PolSAR) image classification. Most CNN-based models, however, concentrate on the correlation between the pixels and the labels in images and have fewer constraints on interclass or intraclass features. For fully utilizing the polarimetric data, we utilized complex-valued (CV) distance to learn the PolSAR features and proposed a PolSAR classification method by CV distance comparisons. First, we proposed a triplet CV network (TCVN) to learn the CV representations from PolSAR data by maximizing the interclass distance and minimizing intraclass distance. It uses the CV convolution and the CV Euclidean to maintain the phase components and applies the CV-dropout and CV parameter regularization to reduce the overfitting and further improve the network performance. Subsequently, CV K nearest neighbor (CV-KNN) computes the distance of the CV representations and groups similar pixels. CV-KNN is well coupled with the TCVN because both of them are based on the Euclidean distance in the complex domain. Compared with the CNN-based methods, the proposed deep metric learning model can simultaneously extract the hierarchical features by comparing the polarimetric resolution cells in the complex domain and maintain the phase component by performing CV convolutions. The effectiveness and the superiorities of CV Euclidean distance in TCVN are demonstrated. Experiments on real PolSAR images illustrate that TCVN can deal with PolSAR data more effectively and achieve comparable performance in the PolSAR image classification even with a smaller data set.
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