Spectral matching accuracy in processing hyperspectral data

2006 ◽  
Author(s):  
S.A. Robila ◽  
A. Gershman
Keyword(s):  
Hyperspectral Data ◽  
Matching Accuracy ◽  
Spectral Matching

Scattering Noise Elimination Method for Improving Spectral Matching Accuracy

2019 ◽  
Vol 56 (2) ◽  
pp. 022401
Author(s):  
王展 Wang Zhan ◽  
王可 Wang Ke ◽  
王伟超 Wang Weichao
Keyword(s):  
Matching Accuracy ◽  
Noise Elimination ◽  
Spectral Matching ◽  
Elimination Method ◽  
Scattering Noise

New effective spectral matching measures for hyperspectral data analysis

2021 ◽  
Vol 42 (11) ◽  
pp. 4126-4156
Author(s):  
Chandan Kumar ◽  
Snehamoy Chatterjee ◽  
Thomas Oommen ◽  
Arindam Guha
Keyword(s):  
Data Analysis ◽  
Hyperspectral Data ◽  
Spectral Matching

scholarly journals Features for Cross Spectral Image Matching: A Survey

2018 ◽  
Vol 7 (4) ◽  
pp. 552-560 ◽  
Author(s):  
Maulisa Oktiana ◽  
Fitri Arnia ◽  
Yuwaldi Away ◽  
Khairul Munadi
Keyword(s):  
Image Matching ◽  
State Of The Art ◽  
Selection Methods ◽  
Matching Accuracy ◽  
Spectral Image ◽  
Spectral Matching ◽  
Extraction Step ◽  
Biometric Systems ◽  
Thermal Features ◽  
Basic Concepts

In recent years, cross spectral matching has been gaining attention in various biometric systems for identification and verification purposes. Cross spectral matching allows images taken under different electromagnetic spectrums to match each other. In cross spectral matching, one of the keys for successful matching is determined by the features used for representing an image. Therefore, the feature extraction step becomes an essential task. Researchers have improved matching accuracy by developing robust features. This paper presents most commonly selected features used in cross spectral matching. This survey covers basic concepts of cross spectral matching, visual and thermal features extraction, and state of the art descriptors. In the end, this paper provides a description of better feature selection methods in cross spectral matching.

Automatic tumor tissue classification of oncological esophageal resectates based on hyperspectral data

2019 ◽  
Author(s):  
M Maktabi ◽  
H Köhler ◽  
R Thieme ◽  
JP Takoh ◽  
SM Rabe ◽  
...  
Keyword(s):  
Tumor Tissue ◽  
Hyperspectral Data ◽  
Tissue Classification

AN AUTOMATIC APPROACH TO LOSSY COMPRESSION OF AVIRIS HYPERSPECTRAL DATA

2010 ◽  
Vol 69 (6) ◽  
pp. 537-563 ◽  
Author(s):  
N. N. Ponomarenko ◽  
M. S. Zriakhov ◽  
A. Kaarna
Keyword(s):  
Lossy Compression ◽  
Hyperspectral Data
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