LU502854B1 - A hyperspectral image band selection method and system based on nearest neighbor subspace division - Google Patents

A hyperspectral image band selection method and system based on nearest neighbor subspace division Download PDF

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LU502854B1
LU502854B1 LU502854A LU502854A LU502854B1 LU 502854 B1 LU502854 B1 LU 502854B1 LU 502854 A LU502854 A LU 502854A LU 502854 A LU502854 A LU 502854A LU 502854 B1 LU502854 B1 LU 502854B1
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bands
band
subspace
hyperspectral image
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LU502854A
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German (de)
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Chang Tang
Jianmin Zhao
Huiying Xu
Xinzhong Zhu
Jun Wang
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Univ Zhejiang Normal
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G06V20/194—Terrestrial scenes using hyperspectral data, i.e. more or other wavelengths than RGB
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/01—Arrangements or apparatus for facilitating the optical investigation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
    • G06F18/24147—Distances to closest patterns, e.g. nearest neighbour classification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N2021/1793—Remote sensing
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A40/00—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production
    • Y02A40/10—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production in agriculture

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  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
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  • Life Sciences & Earth Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Analytical Chemistry (AREA)
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  • Pathology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
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  • Data Mining & Analysis (AREA)
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  • Evolutionary Computation (AREA)
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  • Medical Informatics (AREA)
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  • Spectroscopy & Molecular Physics (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
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  • General Engineering & Computer Science (AREA)
  • Remote Sensing (AREA)
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LU502854A 2021-02-07 2021-12-07 A hyperspectral image band selection method and system based on nearest neighbor subspace division LU502854B1 (en)

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Application Number Priority Date Filing Date Title
CN202110174636.8A CN113075129B (zh) 2021-02-07 2021-02-07 一种基于近邻子空间划分高光谱影像波段选择方法及系统

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CN (1) CN113075129B (zh)
LU (1) LU502854B1 (zh)
WO (1) WO2022166363A1 (zh)
ZA (1) ZA202207737B (zh)

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CN113075129B (zh) * 2021-02-07 2023-03-31 浙江师范大学 一种基于近邻子空间划分高光谱影像波段选择方法及系统
CN113486876A (zh) * 2021-09-08 2021-10-08 中国地质大学（武汉） 一种高光谱影像波段选择方法、装置及系统
CN117435940B (zh) * 2023-12-20 2024-03-05 龙建路桥股份有限公司 一种面向冬季混凝土养护过程中光谱检测方法

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CN101131734A (zh) * 2007-06-25 2008-02-27 北京航空航天大学 适用于高光谱遥感图像的自动波段选择方法
KR100963797B1 (ko) * 2008-02-27 2010-06-17 아주대학교산학협력단 복잡성이 감소된 고분광 프로세싱에 기반을 둔 실시간 타겟검출 방법
CN103065293A (zh) * 2012-12-31 2013-04-24 中国科学院东北地理与农业生态研究所 相关性加权的遥感影像融合方法及该融合方法的融合效果评价方法
CN103886334A (zh) * 2014-04-08 2014-06-25 河海大学 一种多指标融合的高光谱遥感影像降维方法
CN104122210B (zh) * 2014-07-02 2017-01-25 中国林业科学研究院林业研究所 一种基于最佳指数‑相关系数法的高光谱波段提取方法
CN104751179B (zh) * 2015-04-01 2018-02-06 河海大学 一种基于博弈论的多目标高光谱遥感影像波段选择方法
CN107124612B (zh) * 2017-04-26 2019-06-14 东北大学 基于分布式压缩感知的高光谱图像压缩方法
CN108154094B (zh) * 2017-12-14 2020-04-24 浙江工业大学 基于子区间划分的高光谱图像非监督波段选择方法
WO2019183136A1 (en) * 2018-03-20 2019-09-26 SafetySpect, Inc. Apparatus and method for multimode analytical sensing of items such as food
CN110751142B (zh) * 2019-09-25 2022-07-26 河海大学 一种改进型的高光谱遥感影像波段选择方法
CN113075129B (zh) * 2021-02-07 2023-03-31 浙江师范大学 一种基于近邻子空间划分高光谱影像波段选择方法及系统

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CN113075129B (zh) 2023-03-31
ZA202207737B (en) 2022-07-27
CN113075129A (zh) 2021-07-06
WO2022166363A1 (zh) 2022-08-11

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