A Classification Method of Multispectral Remote Sensing Images Based on Spectral and Texture Features

Abstract

The invention discloses a multi-spectral remote sensing image feature classification method based on spectrum and texture features. The method adopts quadtree block technology to perform multi-level block processing on the image, and extracts the spectrum and feature of the feature in the form of image blocks. Texture features, the SVM classifier is used to classify the ground objects of the image block, and the edge area of ​​the image block classification is processed by the region growing method. Compared with the existing technology, this multispectral remote sensing image classification method improves the anti-noise performance of spectral features and texture features in ground object classification, avoids the problem of texture feature extraction window size, and makes the classification results have strong regional consistency and low noise. Less pros.

Description

technical field [0001] The invention belongs to the technical field of digital image processing, and relates to a classification method for multi-spectral remote sensing images, in particular to a method for classifying multi-spectral remote sensing images based on spectral and texture features. A classification method for typical ground objects in high-resolution multispectral remote sensing images. Background technique [0002] Remote sensing images can reflect the situation of ground features in a large area. The classification of ground features based on remote sensing images can be applied to many aspects such as environmental monitoring, resource investigation, land planning, disaster prevention, and ground feature surveying and mapping. Multispectral remote sensing images usually have 4-7 bands. Compared with single-band panchromatic remote sensing images, more information on ground objects in blue, green, red, and near-infrared bands can be obtained, which is conduci...

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Problems solved by technology

[0004] The existing multi-spectral remote sensing image classification methods can better classify the ground objects with small texture granularity and relatively uniform spectrum, but in high-resolution images, the texture granularity of residential areas and mountainous areas is relatively coarse and mixed. A small number of other types of ground objects are not easy to form a large classification area, and the classification area contains a lot of noise and poor consistency

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