The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jan. 20, 2026

Filed:

Jun. 10, 2025
Applicant:

Dalian University of Technology, Liaoning, CN;

Inventors:

Tun Cao, Liaoning, CN;

Jingyuan Jia, Liaoning, CN;

Sixuan Lu, Liaoning, CN;

Chaoyuan Li, Liaoning, CN;

Tailei Wang, Liaoning, CN;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 5/70 (2024.01); G01N 21/31 (2006.01); G01N 33/18 (2006.01); G06F 17/16 (2006.01); G06T 5/20 (2006.01); G06T 7/00 (2017.01);
U.S. Cl.
CPC ...
G01N 21/31 (2013.01); G01N 33/18 (2013.01); G06F 17/16 (2013.01); G06T 5/20 (2013.01); G06T 5/70 (2024.01); G06T 7/0002 (2013.01); G06T 2207/20081 (2013.01);
Abstract

The present invention provides an integrated hyperspectral water quality analysis method, which belongs to the field of hyperspectral water quality analysis. First, data preprocessing is conducted by water quality data collection and water quality image collection in early stage; second, three dimensionality reduction methods are adopted to conduct dimensionality reduction processing, and fused dimensionality reduction is conducted by parameter trade-off selection; third, machine learning algorithms are adopted to train and test hyperspectral water quality inversion models on spectral data after dimensionality reduction; finally, the hyperspectral water quality inversion models are selected and optimized. The present invention adopts an innovative fusion strategy in the aspect of data dimensionality reduction processing, which can achieve a better data dimensionality reduction effect, effectively remove noise and redundant information, and provide a more accurate and reliable data basis.


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