Superpixel-Oriented Unsupervised Classification for Polarimetric SAR Images Based on Consensus Similarity Network Fusion

Abstract

Unsupervised polarimetric synthetic aperture radar (PolSAR) image classification is an important task in PolSAR automatic image analysis and interpretation. Generally, a group of features is insufficient to effectively classify PolSAR images, especially in multiple terrain scenarios. Therefore, multiple features need to be extracted for PolSAR image classification. However, how to combine and integrate these features effectively to fully utilize each feature's information and discriminability need to be determined. Such integrated work has traditionally received little attention. In this paper, a novel unsupervised classification framework for PolSAR images is proposed. First, a PolSAR image is oversegmented via a fast superpixel segmentation method. Second, five feature vectors are extracted from PolSAR images via superpixels, resulting in five corresponding similarity matrices that are constructed by using Gaussian kernels. Third, consensus similarity network fusion (CSNF), originally proposed and widely used for biomedical sciences, is employed to combine and integrate the five similarity matrices to obtain a fused similarity matrix. Fourth, spectral clustering method, based on the fused similarity matrix, is used to cluster the PolSAR image. Finally, a novel classification postprocessing procedure is presented and exploited to smooth the initial clusters and correct some misclassified pixels. The extensive experimental results conducted on one simulated and two real-world PolSAR images demonstrate the feasibility and superiority of the proposed method compared with five other state-of-the-art classification approaches. INDEX TERMS Polarimetric synthetic aperture radar (PolSAR) images, unsupervised classification, superpixels segmentation, consensus similarity network fusion (CSNF), spectral clustering.

Polarimetric SAR image classification using binary coding‐based polarimetric‐morphological features

“…It combines a Markov random field‐based measure with a supervised softmax regression‐based structure. In order to decrease the speckle noise of POLSAR image and benefiting the regional information, a superpixel‐based unsupervised classification method is proposed in [22]. Residual learning is also used to deal with this problem in [23].…”

Superpixel Segmentation for Polsar Images Based on Hexagon Initialization and Edge Refinement

“…Therefore, the interpretation of PolSAR images has been paid extensive attention. Specifically, object-based interpretation processing has received a considerable amount of concentration for reasons of better noise resistance and lower subsequent primitives (Zou et al, 2019). Furthermore, the superpixel segmentation method can effectively generate the controllable number, regular shape and compact regions, which attracts widespread attention.…”

Learning Rotation Domain Deep Mutual Information Using Convolutional LSTM for Unsupervised PolSAR Image Classification

“…Hua et al [25] presented an unsupervised classification algorithm with an adaptive number of classes for PolSAR data, which is capable of automatically estimating the class numbers. Zou et al [26] proposed an unsupervised classification framework for PolSAR images by combining the superpixel segmentation, Gaussian kernels, consensus similarity network fusion, spectral clustering, and a new post-processing procedure. The non-neural machine learning based methods have achieved promising results for unsupervised PolSAR image classification [27,28], but the current methods still suffer some problems.…”

“…First, some methods have cumbersome pipelines, such as pre-processing, feature extraction, clustering, post processing, and so on. For example, superpixel segmentation is usually used to take advantage of the spatial information of pixels [24,26,28,29]. Some methods over-cluster PolSAR images and manually merge the similar classes to improve the performance [24].…”

“…Some methods [25,40] can obtain the optimal number of clusters in the cluster algorithms. Most unsupervised classification methods use a predefined number of classes in both natural image processing and remote sensing image interpretation fields [24,26,30,34,41]. In this paper, we also focus on the unsupervised PolSAR image classification method with a predefined number of classes.…”