High-spectrum image classification method based on nonlinear time series analysis

Abstract

A high-spectrum image classification method based on nonlinear time series analysis is to analyze and process high-spectrum reflectance curves through nonlinear time series analysis so as to perform feature construction for different pixels in high-spectrum images and then finish classification according to constructed features. The method includes: 1, obtaining high-spectrum data to be processed through a man-machine interaction interface, 2, obtaining a feature combination used for ground object classification through a high-spectrum feature construction module, 3, performing ground object classification for cases through a high-spectrum ground object classification module by aid of feature construction results, and 4, outputting classification results through a classification result output module. The high-spectrum image classification method based on nonlinear time series analysis has the advantages of being strong in robustness, small in space complexity, high in classification accuracy and wide in application range, and time complexity and the number of sample points keep linear relation.

Description

technical field [0001] The invention relates to a novel hyperspectral image classification method based on nonlinear time series analysis, which is suitable for a hyperspectral image processing system and belongs to the field of hyperspectral image processing. Background technique [0002] One of the greatest achievements of remote sensing technology in the 1980s was the rise of hyperspectral remote sensing technology, which has been widely used in commercial, military and civilian fields due to the advantages of both imaging and spectral detection. With the improvement of spectral resolution of hyperspectral images, substances that cannot be detected in conventional remote sensing can be detected in hyperspectral remote sensing, which provides a prerequisite for detailed classification of ground objects in the later stage. Although hyperspectral can provide rich ground information, its large number of bands leads to exponentially increasing data volume and information redun...

AI Technical Summary

Problems solved by technology

[0006] 3) Too many spectral segments are selected, which not only increases the amount of computation and affects the calculation speed, but also requires a large number of training samples, otherwise it is difficult to obtain the expected recognition or classification results

[0013] In the hyperspectral data processing system, due to the computational complexity requirements, the classification algorithm should have a small amount of calculation, and the classification accuracy of the traditional algorithm that meets this requirement is poor.

Due to the requirements of classification accuracy, the classification algorithm must have good robustness to different classification scenarios, and the algorithm to achieve this requirement has high computational complexity

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