Remote sensing image land utilization scene classification method based on two-dimension wavelet decomposition and visual sense bag-of-word model

Abstract

The invention relates to a remote sensing image land utilization scene classification method based on two-dimension wavelet decomposition and a visual sense bag-of-word model. The method comprises the steps that a remote sensing image land utilization scene classification training set is built; scene images in the training set are converted to grayscale images, and two-dimension decomposition is conducted on the grayscale images; regular-grid sampling and SIFT extracting are conducted on the converted grayscale images and sub-images formed after two-dimension decomposition, and universal visual word lists of the converted grayscale images and the sub-images are independently generated through clustering; visual word mapping is conducted on each image in the training set to obtain bag-of-word characteristics; the bag-of-word characteristics of each image in the training set and corresponding scene category serial numbers serve as training data for generating a classification model through an SVM algorithm; images of each scene are classified according to the classification model. The remote sensing image land utilization scene classification method well solves the problems that remote sensing image texture information is not sufficiently considered through an existing scene classification method based on a visual sense bag-of-word model, and can effectively improve scene classification precision.

Description

technical field [0001] The invention relates to the technical field of remote sensing image scene classification, in particular to a remote sensing image land use scene classification method based on two-dimensional wavelet decomposition and a visual word bag model. Background technique [0002] With the development of remote sensing technology and the improvement of spatial and temporal resolution, the data volume of remote sensing images, especially high spatial resolution remote sensing images, has increased rapidly, making land use scenes in images contain various types of land cover types. In this case, it takes a lot of time and workload to classify the land use scenes of remote sensing images using the method of manual visual interpretation, and limited experts cannot process the massive data in time. In view of the shortcomings of visual interpretation, automatic and intelligent land use scene classification using computer technology has become a research hotspot in ...

AI Technical Summary

Problems solved by technology

The present invention adds the texture information of the land use scene image in the construction of the visual word bag model by using the two-dimensional wavelet decomposition method, which makes up for the problem of insufficient utilization of the texture information of the remote sensing image by the existing scene classification method based on the visual word bag, and improves classification accuracy

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