Mineral identification method based on full-spectrum-segment hyperspectral remote sensing data

A technology of hyperspectral remote sensing and identification methods, applied in the field of hyperspectral data processing methods and applications

Active Publication Date: 2019-09-20
BEIHANG UNIV
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Problems solved by technology

[0004] The purpose of the present invention is to provide a mineral identification method based on full-spectrum hyperspectral remote sensing data for the problems of less prior information in mineral identification and the identification of different mineral components in different waveband ranges

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  • Mineral identification method based on full-spectrum-segment hyperspectral remote sensing data

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Embodiment Construction

[0033] In order to better illustrate the hyperspectral remote sensing data mineral identification method involved in the present invention, use Hymap visible light to short-wave infrared (VNIR-SWIR), MASI mid-wave infrared (MWIR) and TASI long-wave infrared (LWIR) hyperspectral data to carry out fine mineral identification . A method for classifying hyperspectral remote sensing data based on a deep neural network of the present invention, the specific implementation steps are as follows:

[0034] (1) Read in hyperspectral data of different band ranges: read in the hyperspectral data of visible-short-wave infrared-medium-wave infrared-long-wave infrared in the same area to be processed: take the hyperspectral image of Huitong Mountain in Gansu as the experimental data, and use different bands The range data are obtained by Hymap, MASI and TASI respectively, and the band intervals are 467-2470nm, 3015-4984nm and 8054-11449nm respectively.

[0035] (2) Separating the image with ...

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Abstract

A mineral identification method based on full-spectrum-segment hyperspectral remote sensing data is disclosed. The method comprises the following steps of (1) reading hyperspectral data in different wave band ranges; (2) carrying out minimum noise separation on an image and carrying out data dimensionality reduction; (3) calculating an information entropy of a pixel in a minimum noise separation result obtained in the step (2), setting a threshold value and extracting the pixel with a small information entropy; (4) corresponding the pixel extracted in the step (3) to an original image according to a pixel position, acquiring spectral characteristic parameter, and comparing with and marking the spectral characteristic parameter of a mineral spectral curve in a spectral library; (5) inputting a marked sample into a learning device and training the learning device to obtain a mineral identification result of each single wave band range; and (6) based on a main body majority voting method, fusing each wave band range identification result and completing full-spectrum-segment mineral identification. By using the method of the invention, higher identification accuracy can be acquired when prior information of an identification area is less, and full-spectrum-segment data can be used to make an identification result be comprehensive and accurate.

Description

technical field [0001] The invention relates to a mineral identification method based on full-spectrum hyperspectral remote sensing data, belongs to the field of hyperspectral data processing methods and application technologies, and is suitable for hyperspectral data target identification methods and application technology research. Background technique [0002] Since the development of hyperspectral, the application of hyperspectral images of various single spectral ranges has been widely used, but the technology of using hyperspectral images of the same area in the same area for target recognition still has technical defects, and the application methods are relatively lacking. For the hyperspectral image of an area with less prior information, how to accurately identify the target, and how to use the full spectrum for target recognition to make the recognition result more accurate and the recognized target types more complete are worthy of research and mining. Due to the ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N21/31
CPCG01N21/314
Inventor 李娜赵慧洁黄鑫辰王明聪
Owner BEIHANG UNIV
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