Hyperspectral remote sensing image SVM classification method by combining spectrum and texture features and hyperspectral remote sensing image SVM classification system thereof

Abstract

The invention discloses a hyperspectral remote sensing image SVM classification method by combining spectrum and texture features and a hyperspectral remote sensing image SVM classification system thereof. The method comprises the following steps that S1, original hyperspectral images to be classified and a ground survey data sample set are inputted; S2, the image elements of the corresponding coordinate positions in the original hyperspectral images are extracted so as to form a reference data sample set; S3, a training sample set is randomly selected for each ground feature class; S4, principal component transformation is performed, and first principal component images are extracted; S5, a region segmentation image is acquired; S6, filtering images are acquired; S7, statistics of spectrum feature information and texture feature information of each segmentation region are performed; S8, a support vector machine model is solved; S9, the original hyperspectral images are classified so that the classified hyperspectral images are obtained; and S10, the classified images are outputted. The new strategy for combining the spectrum and texture features is provided so that the hyperspectral image classification precision can be effectively enhanced.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a hyperspectral remote sensing image SVM classification method and system combining spectral and texture features. 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 m...

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is to provide a method for introducing spectral histogram statistics to characterize the texture between different object categories in view of the defect that the rich spatial information contained in the image is not considered in the prior art and the classification accuracy is low. Feature differences, and establish a support vector machine classifier based on a composite kernel, organically combine spectral information with texture features, improve the homogeneity of classification results and classification accuracy, and combine spectral and texture features SVM classification method and system for hyperspectral remote sensing images

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