High spectroscopic data supervision classifying method based on information quantity dimensionality sequence

Abstract

A high spectroscopic data supervision classification method based on information quantity dimensionality sequence includes the following steps: reading the high spectrum image data of a certain region; selecting a reference spectrum from the spectrum library or selecting the training sample from images to execute wave band average acquiring reference spectrum; calculating one by one the reference spectrum and all test spectral information quantity dimensionality sequence; executing vector angle matching one by one the test spectrum with all reference spectral information quantity dimensionality sequence, and using minimum distance classification machine to classify; result binarization matching, the matching result of each series of field culture is represented by the binary images, each image only includes a series of field culture. The high spectroscopic data classification method based on information quantity dimensionality sequence introduces the information quantity dimensionality into the spectrum domain analysis, synthetizes the advantages of the all band matching and partial quantization characteristic matching, can obtain higher classification effectiveness and classification accuracy, and has important value in the high spectroscopic data classification and object identification.

Description

technical field [0001] The invention relates to a supervised classification method for hyperspectral data based on an information dimension sequence, which belongs to the field of hyperspectral data processing methods and application technologies, and is suitable for research on theoretical methods and applied technologies for hyperspectral data target recognition and supervised classification. Background technique [0002] Target recognition and ground object classification are one of the main directions of hyperspectral remote sensing data application. Hyperspectral images provide a nearly continuous spectrum to achieve accurate descriptions of targets. Compared with other remote sensing methods, the biggest advantage of hyperspectral remote sensing technology is reflected in the ability to classify and identify ground objects. The development and expansion of this type of technology The depth and breadth of hyperspectral data applications are enhanced. The classification...

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Problems solved by technology

[0005] The purpose of the hyperspectral data supervised classification method based on the information dimension sequence of the present invention and the problem to be solved are: introduce the information dimension into spectral domain analysis, and apply it to target recognition and supervised classification of hyperspectral data Integrating the advantages of full-band matching and local quantitative feature matching, it overcomes the disadvantages of using a single matching method that is not sensitive to small differences, or is greatly affected by signal-to-noise ratio and spectral reconstruction accuracy, so as to obtain higher classification efficiency and classification accuracy

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