Rapid hyperspectral image clustering method and device, equipment and medium
A technology of hyperspectral image and clustering method, applied in the field of fast hyperspectral image clustering, which can solve problems such as difficulty and reduce computational complexity
Pending Publication Date: 2020-10-09
GUANGDONG UNIV OF TECH
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[0006] This application provides a fast hyperspectral image clustering method, device, equipment and medium, which solves the technical problem that it is difficult to reduce the computational complexity while ensuring the characteristics between data points in the prior art
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[0068] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiment of the application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiment of the application. Obviously, the described embodiment is only It is a part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the scope of protection of this application.
[0069] figure 1 It is a method flowchart of an embodiment of a fast hyperspectral image clustering method of the present application, such as figure 1 as shown, figure 1 Include:
[0070] 101. Acquire an original hyperspectral image.
[0071] It should be noted that a hyperspectral image can be regarded as a three-dimensional data cube with two spatial dimensio...
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The invention discloses a rapid hyperspectral image clustering method and device, equipment and a medium. The method comprises the steps of acquiring an original hyperspectral image; selecting a plurality of anchor points from the data points of the hyperspectral image to form a first anchor point graph, and sequentially constructing a plurality of anchor point graphs; constructing a layered anchor point graph by using the constructed anchor point graphs; calculating an interlayer adjacent matrix in the layered anchor point graph by using a Gaussian kernel function; constructing a bipartite graph by using the data points of the hyperspectral image and the last layer of the layered anchor graph, and constructing a similarity matrix and a diagonal matrix of the bipartite graph by using the interlayer adjacent matrix so that the Laplace matrix is obtained by subtracting the similarity matrix from the diagonal matrix; constructing an objective function of hyperspectral clustering by usingthe Laplace matrix; and solving the target function, and calculating a solving result by adopting a k-means clustering method to complete the clustering of the hyperspectral image. According to the invention, the technical problem that the calculation complexity is difficult to reduce while the characteristics between the data points are ensured in the prior art is solved.
Description
technical field [0001] The present application relates to the technical field of image clustering, in particular to a fast hyperspectral image clustering method, device, equipment and medium. Background technique [0002] A hyperspectral image can be regarded as a three-dimensional data cube with two spatial dimensions and one spectral dimension. Generally, a hyperspectral image has the characteristics of multiple bands, narrow band width, and high spectral resolution, but it also has the difficulty of high feature dimension. In recent years, due to the characteristics of hyperspectral data and the rich information it contains, the analysis and processing of hyperspectral images has become one of the hotspots in the field of remote sensing image research. domain plays a substantively important role and deserves a more in-depth study. [0003] Spectral clustering algorithm is a graph-based clustering algorithm, which can optimally partition data of any shape. It is one of t...
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IPC IPC(8): G06K9/62G01N21/17
CPCG01N21/17G01N2021/1793G06F18/23213
Inventor 杨晓君黄晓蓓郭春炳许裕雄钟浩宇杜晓颜
Owner GUANGDONG UNIV OF TECH