Aug 8, 2016 · Unsupervised classification is very important for the fully polarimetric synthetic aperture radar (PolSAR) image interpretation.
Unsupervised classification is very important for the fully polarimetric synthetic aperture radar (PolSAR) image interpretation. The PolSAR covariance ...
In this paper, we propose an unsupervised classification method by introducing a sparsity based similarity measure on HPD matrices. Specifically, we first use a ...
Nov 26, 2024 · Reports Outline Geoscience Findings from Wuhan University (Unsupervised Classification of PolSAR Imagery via Kernel Sparse Subspace Clustering) ...
In this paper, we propose the novel subspace cluster based methods to process the PolSAR imagery classification. It analyzes the extracted features and ...
This paper proposes an unsupervised classification method by introducing a sparsity-based similarity measure on Hermitian positive definite (HPD) covariance ...
Apr 25, 2024 · Unsupervised Classification of PolSAR Imagery via Kernel Sparse Subspace Clustering. IEEE Geosci. Remote. Sens. Lett. 13(10): 1487-1491 ...
This paper combines sparse representation, low-rank representation, and manifold graphs to investigate the intrinsic property of PolSar data and ...
The proposed algorithms aim at constructing a projection matrix from the subspace clustering algorithms to achieve the features benefiting for the subsequent ...
This study proposes a new unsupervised PolSAR image classification framework. The framework combines high-confidence superpixel pseudo-labeled samples and semi ...
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