Hyperspectral band selection evaluation method based on independent component analysis
An independent component analysis and independent component technology, applied in the fields of instruments, character and pattern recognition, computer parts, etc., can solve the problems such as the increase in the number of training samples of feature dimension, the reduction in the improvement of classification accuracy, and the low efficiency. Effectiveness
Pending Publication Date: 2022-04-12
BEIJING INST OF ENVIRONMENTAL FEATURES
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[0002] Hyperspectral remote sensing image data can provide a wealth of spectral information, but in the classification of ground objects, if all the features of the image are used, it will lead to lower efficiency and greater computational complexity, and the increase in the feature dimension will also make the The number of training samples keeps increasing
If the number of training samples is relatively small and the feature dimension is high, it will lead to a decrease in the improvement of classification accuracy.
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[0028] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.
[0029] figure 1 It is a schematic diagram of the main steps of the hyperspectral band selection evaluation method based on independent component analysis in the embodiment of the present invention.
[0030] Such as figure 1 As shown, the hyperspectral band selection evaluation method based on independent component analysis in the embodiment...
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The invention relates to a hyperspectral band selection evaluation method based on independent component analysis, and one specific implementation mode of the method comprises the steps: for a to-be-evaluated hyperspectral image after band selection, determining information of a plurality of independent components in the hyperspectral image; calculating skewness and kurtosis combined statistics of each piece of independent component information, and determining the independent component information with the skewness and kurtosis combined statistics greater than a preset threshold as target independent component information of the hyperspectral image; and according to the proportion of the target independent component information in the multiple pieces of independent component information, judging the accuracy degree of band selection. According to the embodiment, the accuracy degree of hyperspectral image band selection can be accurately evaluated.
Description
technical field [0001] The invention relates to the field of hyperspectral data processing, in particular to a hyperspectral band selection evaluation method based on independent component analysis. Background technique [0002] Hyperspectral remote sensing image data can provide a wealth of spectral information, but in the classification of ground objects, if all the features of the image are used, it will lead to lower efficiency and greater computational complexity, and the increase in the feature dimension will also make the The number of training samples keeps increasing. If the number of training samples is relatively small and the feature dimension is high, it will lead to a decrease in the improvement of classification accuracy. When the dimensionality of hyperspectral remote sensing image data increases, the classification accuracy will first increase and then decrease Hughes phenomenon. In practical applications, the effective information of hyperspectral remote ...
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Login to View More IPC IPC(8): G06V10/77G06K9/62G06V10/764
Inventor 刘畅王广平
Owner BEIJING INST OF ENVIRONMENTAL FEATURES