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In this work we derive a novel clustering scheme for hyperspectral pixels according to the material they sense. We utilize statistical correlations that pixels sensing the same material exhibit. Specifically, kernel learning is combined with a norm-one regularized canonical correlations framework that can perform data clustering on nonlinearly dependent data. To tackle the derived minimization formulation...
In this paper, a novel spectral-spatial low-rank subspace clustering (SS-LRSC) algorithm is presented for clustering of hyperspectral images (HSI). Generally, employing the traditional LRSC framework directly cannot fully exploit the sample correlations in original spatial domain. Therefore, the proposed method utilizes a novel modulation strategy to modify the low rank representation matrix, which...
Superpixel has been widely applied in hyperspectral image processing as a pre-processing step for over-segmentation. However, most superpixel algorithms are difficult to control the segmentation balance between fragmentation and accuracy. In this paper, we propose a superpixel aggregation model to cluster the over-segmentations. Based on the own importance and interrelationship of superpixels, a two-step...
Hyperspectral image clustering is commonly applied for unsupervised classification. However, the clustering results of traditional methods are not sufficient seeing the nature of the image as a data cube with high dimensionality. In addition, the complex relations between spatial neighboring pixels are not considered in traditional methods. In this paper the fuzzy c-means clustering is revisited and...
Band selection is an effective approach to mitigate the “Hughes phenomenon” of hyperspectral image (HSI) classification. In this paper, a novel squaring weighted low-rank subspace clustering band selection (SWLRSC) algorithm is proposed for hyperspectral imagery. The SWLRSC method can effectively capture the global structure information of the HSI band set by constructing a strongly connected adjacency...
In this paper a joint spectral unmixing and clustering approach for the identification of homogeneous regions in hyperspectral images is proposed. The endmembers required in the unmixing stage are manually selected based on the most significant principal components of the image at hand. Each pixel is decomposed as a linear combination of the endmembers and is represented by the vector of the coefficients...
Clustering for hyperspectral imagery (HSI) is a very challenging task due to its inherent spectral and spatial complexity. In this paper, we propose a novel spectral-spatial sparse subspace clustering (S4C) algorithm for hyperspectral imagery. Firstly, by treating each kind of ground class as a subspace, we introduce sparse subspace clustering (SSC) algorithm to HSIs. Then considering the spectral...
In this manuscript, we propose a sparse self-representation (SSR) method to select a band subset in hyperspectral imagery (HSI) classification. The SSR method improves from multiple measurement vectors (MMV) with the measurement matrix equals to the observation matrix. The SSR regards that each band could be represented as a linear combination of the representatives of all bands and accordingly all...
Spectral unmixing aims to estimate the fractional abundances of spectral signatures in each pixel. The Linear Mixing Model (LMM) of hyperspectral images assumes that pixel spectra are affine combinations of fundamental spectral signatures called endmembers. Endmember induction algorithms (EIA) extract the endmembers from the hyperspectral data. The WM algorithm assumes that a set of Affine Independent...
An unsupervised band selection method for hyperspectral images is proposed in this article. Three steps are followed to carry out the algorithm. In the first step, characteristics (attributes) of the bands are generated. Next, redundancy among the bands is removed by using clustering. DBSCAN algorithm is used for clustering the bands. One representative band is selected from each cluster. Finally,...
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