Hyperspectral image low-rank representation clustering method based on bilateral weighting modulation and filtering

Abstract

The invention discloses a hyperspectral image low-rank representation clustering method based on bilateral weighting modulation and filtering. The method comprises the following steps: calculating a low-rank representation coefficient of a hyperspectral image; combining similarity of a spectrum and the low-rank representation coefficient, calculating a bilateral weighting matrix; using the bilateral weighting matrix to modulate the low-rank representation coefficient; performing bilateral filtering on the modulated low-rank representation coefficient; using the filtered low-rank representation coefficient to establish a similarity image; using the similarity image in spectral clustering, to obtain a final clustering result. The method makes full use of spectrum similarity of hyperspectrum and space structure information. Compared with a conventional subspace clustering method, the method is higher in clustering precision, and has higher robustness on noise. The method can be widely applied in non-supervised classification in fields of territorial resources, mineral survey, and precision agriculture.

Description

technical field [0001] The invention relates to remote sensing image processing technology, in particular to a hyperspectral image low-rank representation clustering method based on bilateral weighted modulation and filtering. Background technique [0002] Due to its spectral correlation and rich spatial information, hyperspectral images are widely used in military monitoring, precision agriculture and mineral monitoring, among which hyperspectral image clustering is one of the most important research contents. The basic principle of hyperspectral image clustering is to distinguish and identify target images based on image clustering and combined with the spectral characteristics of hyperspectral images. The theoretical basis is that the same pixel has the same or similar spectral and spatial characteristics, and conversely, different pixels correspond to different spectral and spatial characteristics. [0003] At present, many subspace clustering algorithms for hyperspectr...

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Problems solved by technology

[0004] However, both sparse subspace clustering and low-rank subspace clustering only use the correlation of hyperspectral spectral information, without effective joint space-spectral information, the clustering accuracy is low, and the algorithm performance is low when the data is noisy. decline

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