Spatial information adaptive fusion-based hyperspectral image classification method

Abstract

The invention provides a spatial information adaptive fusion-based hyperspectral image classification method. According to the method, spectral information is extracted as spatial information by usingtwo filters and is classified, so that spatial texture information and correlation information are effectively utilized; through domain transformation standard convolution filtering, a certain spatial texture information can be extracted, and relatively good spatial correlation is kept, so that the deficiency that only the spatial texture information can be extracted through bilateral filtering is made up for; and after adaptive fusion of the spatial texture information extracted through the bilateral filtering and the domain transformation standard convolution filtering, optimal classification performance is obtained through SVM classification, and a non-uniform phenomenon is effectively eliminated, so that the method is especially suitable for hyperspectrum with relatively numerous ground objects and complex distribution.

Description

technical field [0001] The invention relates to the field of digital image processing, in particular to a hyperspectral image classification method for adaptive fusion of spatial information. Background technique [0002] Space-spectrum combination to improve the classification performance of hyperspectral images is a research hotspot at present, and the spatial information extraction methods mainly include: 1) Morphological filter feature extraction, 2) Markov random field feature extraction, 3) Image segmentation feature extraction, among which It has become a research hotspot that filters extract image texture information to assist spectral information for effective classification. [0003] Some scholars use Gabor filter to extract texture information to assist hyperspectral classification. Among them, multi-dimensional Gabor filter is used to extract image texture information from multiple angles, and the classification accuracy is improved; Gabor filter is also used to ...

AI Technical Summary

Problems solved by technology

[0006] 1) Insufficient spatial information is obtained during hyperspectral classification;

[0007] 2) It is difficult to obtain complete spatial information from a single spatial texture information;

[0008] 3) Using filters to extract texture features is easy to lose pixel spatial correlation information

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