Classification of Hyperspectral Reflectance Images With Physical and Statistical Criteria

A classification method of hyperspectral reflectance images named CHRIPS (Classification of Hyperspectral Reflectance Images with Physical and Statistical criteria) is presented. This method aims at classifying each pixel from a given set of thirteen classes: unidentified dark surface, water, plasti...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2020-07, Vol.12 (14), p.2335
Main Authors: Alakian, Alexandre, Achard, Véronique
Format: Article
Language:English
Subjects:
Algorithms
Asphalt
Carbonation
Classification
Computer Science
Criteria
Engineering Sciences
Gravel
hyperspectral
Image classification
Machine learning
Mathematics
Metal surfaces
Morphology
Physics
Pixels
Reflectance
reject class
Remote sensing
Sensors
specific absorption
Spectra
spectral index
Spectral reflectance
Statistics
Vegetation
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Summary:A classification method of hyperspectral reflectance images named CHRIPS (Classification of Hyperspectral Reflectance Images with Physical and Statistical criteria) is presented. This method aims at classifying each pixel from a given set of thirteen classes: unidentified dark surface, water, plastic matter, carbonate, clay, vegetation (dark green, dense green, sparse green, stressed), house roof/tile, asphalt, vehicle/paint/metal surface and non-carbonated gravel. Each class is characterized by physical criteria (detection of specific absorptions or shape features) or statistical criteria (use of dedicated spectral indices) over spectral reflectance. CHRIPS input is a hyperspectral reflectance image covering the spectral range [400–2500 nm]. The presented method has four advantages, namely: (i) is robust in transfer, class identification is based on criteria that are not very sensitive to sensor type; (ii) does not require training, criteria are pre-defined; (iii) includes a reject class, this class reduces misclassifications; (iv) high precision and recall, F 1 score is generally above 0.9 in our test. As the number of classes is limited, CHRIPS could be used in combination with other classification algorithms able to process the reject class in order to decrease the number of unclassified pixels.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs12142335