Abstract
Deep learning can archive state-of-the-art performance in polarimetric synthetic aperture radar (PolSAR) image classification with plenty of labeled data. However, obtaining large number of accurately labeled samples of PolSAR data is very hard, which limits the practical use of deep learning. Therefore, unsupervised PolSAR image classification is worthy of further investigation that is based on deep learning. Inspired by the superior performance of deep mutual information in natural image feature learning and clustering, an end-to-end Convolutional Long Short Term Memory (ConvLSTM) network is used in order to learn the deep mutual information of polarimetric coherent matrices in the rotation domain with different polarimetric orientation angles (POAs) for unsupervised PolSAR image classification. First, for each pixel, paired “POA-spatio” samples are generated from the polarimetric coherent matrices with different POAs. Second, a special designed ConvLSTM network, along with deep mutual information losses, is used in order to learn the discriminative deep mutual information feature representation of the paired data. Finally, the classification results can be output directly from the trained network model. The proposed method is trained in an end-to-end manner and does not have cumbersome pipelines. Experiments on four real PolSAR datasets show that the performance of proposed method surpasses some state-of-the-art deep learning unsupervised classification methods.
Highlights
Polarimetric synthetic aperture radar (PolSAR) is a side-looking active imaging system and it has the advantages of working all day and night, working under all weather conditions, large scope, and certain penetration capacity
We focus on the unsupervised PolSAR image classification method with a predefined number of classes
It again shows that the polarimetric matrix rotation is helpful for deep mutual information learning and it can improve the performance of unsupervised PolSAR image classification
Summary
Polarimetric synthetic aperture radar (PolSAR) is a side-looking active imaging system and it has the advantages of working all day and night, working under all weather conditions, large scope, and certain penetration capacity. Supervised PolSAR image classification has achieved excellent performance. Many traditional statistical model-based methods and non-neural machine learning [4] methods can achieve good results, such as the CoAS model [5], random forest (RF) [6], support vector machine (SVM) [7], and XGBoost [8]. In [9], two mixture models were proposed for modeling heterogeneous regions in single-look and multi-look polarimetric SAR images, along with their corresponding maximum likelihood classifiers for land cover classification. Feng et al [10] proposed a classification scheme for forest growth stage types and other cover types while using a SVM that was based on the Polarimetric
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