Light spectrum and spatial information bonded high spectroscopic data classification method
A technology for spatial information and data classification, applied in the field of unsupervised classification of hyperspectral data, can solve problems such as starting from a single aspect
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Publication Date
- 2010-02-17
- Estimated Expiration
- Not applicable · inactive patent
Smart Images
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Abstract
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. [...
Examples
Embodiment Construction
[0045] In order to better illustrate the hyperspectral data classification method based on the combination of spectral and spatial information involved in the present invention, PHI airborne hyperspectral data is used to carry out fine classification of crops in Fanglu tea farm area, Jiangsu. A hyperspectral data classification method combining spectral and spatial information according to the present invention, the specific implementation steps are as follows:
[0046] (1) Reading in hyperspectral data: read in the PHI hyperspectral data of Fanglutuchang;
[0047] (2) Determine the minimum size of structural elements: According to the characteristics of data and algorithms, the minimum size of structural elements is 3×3;
[0048] (3) Calculate the difference between pixels in the neighborhood of each structural element by expanding and eroding mathematical morphology;
[0049] In order to achieve more reliable, stable and accurate classification of hyperspectral data, the me...