Hyperspectral image classification method based on edge preservation and graph cut model

Abstract

The invention discloses a hyperspectral image classification method based on edge preservation and a graph cut model. The hyperspectral image classification method comprises the following steps that S1, hyperspectral images to be classified are inputted; S2, the image elements of the corresponding coordinate positions of the original hyperspectral images are extracted to form a reference data sample set; S3, a supervised classification training sample set is selected; and the rest reference data samples act as a test sample set; S4, pixel level image classification operation is performed so that a probability membership distribution graph of each corresponding class is acquired; S5, filtering is performed so that the optimized class probability membership distribution graph is acquired; S6, all the ground targets are extracted: the optimized class probability membership distribution graph is cut by using the graph cut model so that the cut result of each class is acquired; and the final tag result is acquired from the cut result of each class by using the merging rule and the maximum posterior probability estimation; and S7, the final classification graph is outputted. A new strategy for area tagging is provided so that the hyperspectral image classification accuracy can be effectively enhanced.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a hyperspectral image classification method based on an edge-preserving and graph-cut model. Background technique [0002] Compared with multispectral remote sensing images, hyperspectral remote sensing images have richer spectral and spatial information, which can accurately reflect the attribute differences between different types of ground objects, and realize accurate extraction and identification of ground objects, providing a higher precision It has laid a good foundation for the analysis and application of hyperspectral remote sensing images. However, image features such as high dimensionality, large band correlation, noise, and unique nonlinear features of hyperspectral imagery have brought great challenges to the analysis and processing of hyperspectral remote sensing imagery. Traditional hyperspectral remote sensing image classification methods usually only us...

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is to provide a probability distribution with optimized categories for the defects of low classification accuracy in the prior art, which can reduce the division errors of the interior of the homogeneous area and the b

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