2019
DOI: 10.1080/01431161.2019.1643939
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Unsupervised classification for PolSAR images based on multi-level feature extraction

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Cited by 9 publications
(6 citation statements)
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“…Surface classification and land use are among the most critical applications of the Synthetic Aperture Radar (SAR) image [5]. In recent years, handcrafted features and representation learning (supervised and unsupervised) algorithms have been proposed [6]- [8]. Algorithms of the unsupervised generative adversarial network (GAN) have revolutionized the classification of SAR images, improving performance in small sample problems, and helping the interpretability of such data [9].…”
Section: Introductionmentioning
confidence: 99%
“…Surface classification and land use are among the most critical applications of the Synthetic Aperture Radar (SAR) image [5]. In recent years, handcrafted features and representation learning (supervised and unsupervised) algorithms have been proposed [6]- [8]. Algorithms of the unsupervised generative adversarial network (GAN) have revolutionized the classification of SAR images, improving performance in small sample problems, and helping the interpretability of such data [9].…”
Section: Introductionmentioning
confidence: 99%
“…These drawbacks make the classification of PolSAR images extremely cumbersome (Zhang, 2008;Zhou et al, 2016;Wang et al, 2021). The classification of PolSAR images is usually challenging (Han et al, 2020;Parikh et al, 2020;Shang et al, 2020;Gopal Singh et al, 2021). To overcome these challenges, many prominent machine learning algorithms like Artificial Neural Network (ANN), Support Vector Machine (SVM), K Means (KM), K Nearest Neighbour (KNN), Gaussian Mixture Model (GMM), Ensemble Learning (EL), Linear Discriminative Laplacian Eigenmaps (LDLE) have been applied to PolSAR image classification (Attarchi, 2020;Parikh et al, 2020;Wang et al, 2021).…”
Section: Introductionmentioning
confidence: 99%
“…In this paper, a feature extraction method is proposed to finding an optimal direction that can map features from the high-dimensional space into lower-dimensional. On the other hand, in [9] In this paper, we propose a method in order to reduce the feature's dimention that are extracted from 3D-Gabor filters [11]. So, at first, we use the PolSAR features that are extracted from scattering matrix of PolSAR images as the input of the 3D-Gabor filter and then we decrease the features that are obtained from 3D-Gabor filters in order to increase the accuracy of the PolSAR image classification.…”
Section: Introductionmentioning
confidence: 99%