Light spectrum and spatial information bonded high spectroscopic data classification method

Abstract

Disclosed is a hyperspectral data classification method which is combined spectrum and spatial information. The steps comprises (1) reading the hypersectral data, (2) confirming the minimum size of structural element, (3) calculating differentiation between picture elements in neighborhood of each structural element by extended mathematical morphology expansion and corrosion operation, (4) obtaining exponential value of morphology eccentricity by the extended expansion and the corrosion operation of step (3), (5), constantly repeating the above steps with the adding of the size of the structural element to achieve the maximum size of the structural element, (6), constantly updating the exponential value MEI of morphology eccentricity in iteration process via the obtained new value, and generating a final exponential value MEI of morphology eccentricity after the iteration process is finished, (7) realizing the extraction of the data characteristic by the image of the exponential valueMEI of morphology eccentricity, namely generating ground object type information, and realizing sophisticated category of the ground object by a minimum-distance classifier. The method is an unsupervised classification method for hyperspectral ground object with strong stability, high reliability and high accuracy.

Description

(1) Technical field [0001] The invention relates to a hyperspectral data classification method using spectral and spatial information at the same time, belongs to the field of hyperspectral data processing methods and application technologies, and is suitable for the theoretical method and application technology research of hyperspectral data unsupervised classification. (2) Background technology [0002] The hyperspectral imager is a new type of remote sensing payload. Its spectrum is compact and continuous, and it can simultaneously record the spectral and spatial information characteristics of the same ground object. Can be detected in spectral remote sensing. Target detection and ground object classification are one of the main directions of hyperspectral remote sensing data application. The development of this type of technology can greatly promote the application of hyperspectral data and continuously expand the application depth and breadth of hyperspectral data. [...

AI Technical Summary

Problems solved by technology

[0008] The purpose of the present invention is to propose a hyperspectral data classification method combining spectral and spatial information, which overcomes the shortcomings of the existing unsupervised classification of ground objects from a single aspect of data spectrum or space or feature information. , which effectively suppresses the influence of the background, it is an unsupervised classification method for hyperspectral objects with strong stability, high reliability and high accuracy

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