Multispectral remote sensing image land feature classification method based on spectrum and textural features

Abstract

The invention discloses a multispectral remote sensing image terrain classification method based on spectrum and textural features. A quadtree partitioning technology is adopted in the method to carry out multistage partitioning processing on images, spectrum and textural features of land features are extracted in an image block mode, an SVM classifier is adopted to conduct land feature classification on image blocks, and classification marginal regions of the image blocks are processed through a region growing method. Compared with the prior art, the multispectral remote sensing image terrain classification method has the advantages that anti-noise performance of spectral signatures and textural features in land feature classification is improved, the problem of sizes of textural feature extraction windows is avoided, and classification result regions are high in consistency and low in noise.

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...

AI Technical Summary

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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