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

Info

Publication number
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
Authority
LU
Luxembourg
Prior art keywords
denotes
bands
band
subspace
hyperspectral image
Prior art date
Application number
LU502854A
Other languages
German (de)
English (en)
Inventor
Chang Tang
Jianmin Zhao
Huiying Xu
Xinzhong Zhu
Jun Wang
Original Assignee
Univ Zhejiang Normal
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Univ Zhejiang Normal filed Critical Univ Zhejiang Normal
Application granted granted Critical
Publication of LU502854B1 publication Critical patent/LU502854B1/en

Links

Classifications

    • 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

Landscapes

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

Applications Claiming Priority (1)

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

Publications (1)

Publication Number Publication Date
LU502854B1 true LU502854B1 (en) 2023-01-30

Family

ID=76609942

Family Applications (1)

Application Number Title Priority Date Filing Date
LU502854A LU502854B1 (en) 2021-02-07 2021-12-07 A hyperspectral image band selection method and system based on nearest neighbor subspace division

Country Status (4)

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

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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

Family Cites Families (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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

Also Published As

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

Similar Documents

Publication Publication Date Title
LU502854B1 (en) A hyperspectral image band selection method and system based on nearest neighbor subspace division
Wang et al. Hyperspectral band selection via adaptive subspace partition strategy
Dang et al. Local outlier detection with interpretation
Chen et al. A clustering algorithm for multiple data streams based on spectral component similarity
Alshammari et al. Refining a k-nearest neighbor graph for a computationally efficient spectral clustering
CN106778680B (zh) 一种基于关键波段提取的高光谱图像波段选择方法及装置
CN103403704A (zh) 用于查找最近邻的方法和设备
Duin et al. Mode seeking clustering by KNN and mean shift evaluated
CN104820840A (zh) 基于字典和波段重组的最近邻高光谱图像分类方法
Dang et al. Outlier detection with space transformation and spectral analysis
CN109871768B (zh) 基于共享最近邻的高光谱最优波段选择方法
CN108257148B (zh) 特定对象的目标建议窗口生成方法及其在目标跟踪的应用
Datta et al. Clustering based band selection for hyperspectral images
CN112884035A (zh) 一种噪声鲁棒的高光谱图像波段选择方法
Le Bris et al. A random forest class memberships based wrapper band selection criterion: Application to hyperspectral
Habermann et al. Unsupervised hyperspectral band selection using clustering and single-layer neural network
Saeidi et al. Deep learning based on parallel CNNs for pedestrian detection
CN104123538A (zh) 一种基于视觉词袋的网络不良图像检测方法
CN114663770A (zh) 一种基于集成聚类波段选择的高光谱图像分类方法及系统
Jain et al. An unsupervised band selection method for hyperspectral images using mutual information based dependence index
Moon et al. Image patch analysis and clustering of sunspots: A dimensionality reduction approach
Demidova et al. Improving the accuracy of the SVM classification using the Parzen classifier
Gu et al. Spatial-spectral multiple kernel learning for hyperspectral image classification
Borhani et al. Hyperspectral image classification based on non-uniform spatial-spectral kernels
CN111539470A (zh) 图像处理方法、装置、计算机设备及存储介质

Legal Events

Date Code Title Description
FG Patent granted

Effective date: 20230130