Remote sensing image classification method based on texton

Abstract

The invention discloses a remote sensing image classification method based on textons. The remote sensing image classification method based on the textons comprises the following steps: selecting the remote sensing images of typical surface features as a first training set and a second training set; extracting the neighborhood feature vectors of similar surface feature images in the first training set, clustering the neighborhood feature vectors to form a texton, and forming a texton dictionary by the textons of different surface features; marking the neighborhood feature vectors of images in the second training set by using the texton dictionary, binning center pixels, and counting the two-dimensional joint distribution of the center pixel-texton of each image to form a texture model base; and dividing images to be classified into superpixels, counting the two-dimensional joint distribution of the center pixel-texton of each superpixel after Laplace calibration is carried out, and comparing texture models of the superpixels with models in the texture model base to classify the superpixels so as to realize image classification. By taking advantages of the high homogeneity of the superpixels and the spatial distribution regularities of textures, the method has high classification accuracy, and exhibits high adaptability and interference immunity.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, and more specifically relates to a remote sensing image classification method based on texture primitives. Background technique [0002] With the development of remote sensing imaging technology and the improvement of the imaging resolution of multi-source images such as satellite visible light, multispectral and hyperspectral images, high-resolution remote sensing images have begun to be widely used in various fields. As an important appearance feature of a scene, texture provides important information for visual perception. Studies have shown that 80% of the information in large-scale scene images is texture information, so texture analysis is an important means to describe image scenes. [0003] Traditional texture features, such as co-occurrence matrix, run length, etc., are artificially extracted from the perspective of signal and feature space. When the texture of v...

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Problems solved by technology

[0006] In view of the above defects or improvement needs of the prior art, the present invention provides a remote sensing image classification method based on texture primitives, aiming to solve the classification problem of optical remote sensing images of the same scene under different time phases and different atmospheric environment parameters.

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