Classification of Hyperspectral or Trichromatic Measurements of Ocean Color Data into Spectral Classes

We propose a method for classifying radiometric oceanic color data measured by hyperspectral satellite sensors into known spectral classes, irrespective of the downwelling irradiance of the particular day, i.e., the illumination conditions. The focus is not on retrieving the inherent optical propert...

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Main Authors: Dilip K. Prasad, Krishna Agarwal
Format: Article
Language:English
Published: MDPI AG 2016-03-01
Series:Sensors
Subjects:
Online Access:http://www.mdpi.com/1424-8220/16/3/413
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author Dilip K. Prasad
Krishna Agarwal
author_facet Dilip K. Prasad
Krishna Agarwal
author_sort Dilip K. Prasad
collection DOAJ
description We propose a method for classifying radiometric oceanic color data measured by hyperspectral satellite sensors into known spectral classes, irrespective of the downwelling irradiance of the particular day, i.e., the illumination conditions. The focus is not on retrieving the inherent optical properties but to classify the pixels according to the known spectral classes of the reflectances from the ocean. The method compensates for the unknown downwelling irradiance by white balancing the radiometric data at the ocean pixels using the radiometric data of bright pixels (typically from clouds). The white-balanced data is compared with the entries in a pre-calibrated lookup table in which each entry represents the spectral properties of one class. The proposed approach is tested on two datasets of in situ measurements and 26 different daylight illumination spectra for medium resolution imaging spectrometer (MERIS), moderate-resolution imaging spectroradiometer (MODIS), sea-viewing wide field-of-view sensor (SeaWiFS), coastal zone color scanner (CZCS), ocean and land colour instrument (OLCI), and visible infrared imaging radiometer suite (VIIRS) sensors. Results are also shown for CIMEL’s SeaPRISM sun photometer sensor used on-board field trips. Accuracy of more than 92% is observed on the validation dataset and more than 86% is observed on the other dataset for all satellite sensors. The potential of applying the algorithms to non-satellite and non-multi-spectral sensors mountable on airborne systems is demonstrated by showing classification results for two consumer cameras. Classification on actual MERIS data is also shown. Additional results comparing the spectra of remote sensing reflectance with level 2 MERIS data and chlorophyll concentration estimates of the data are included.
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spelling doaj.art-a83b1c321cee47febd3f8f3cf752ac862022-12-22T02:55:14ZengMDPI AGSensors1424-82202016-03-0116341310.3390/s16030413s16030413Classification of Hyperspectral or Trichromatic Measurements of Ocean Color Data into Spectral ClassesDilip K. Prasad0Krishna Agarwal1School of Computer Engineering, Nanyang Technological University, Singapore 639798, SingaporeSingapore-MIT Alliance for Research and Technology, Singapore 138602, SingaporeWe propose a method for classifying radiometric oceanic color data measured by hyperspectral satellite sensors into known spectral classes, irrespective of the downwelling irradiance of the particular day, i.e., the illumination conditions. The focus is not on retrieving the inherent optical properties but to classify the pixels according to the known spectral classes of the reflectances from the ocean. The method compensates for the unknown downwelling irradiance by white balancing the radiometric data at the ocean pixels using the radiometric data of bright pixels (typically from clouds). The white-balanced data is compared with the entries in a pre-calibrated lookup table in which each entry represents the spectral properties of one class. The proposed approach is tested on two datasets of in situ measurements and 26 different daylight illumination spectra for medium resolution imaging spectrometer (MERIS), moderate-resolution imaging spectroradiometer (MODIS), sea-viewing wide field-of-view sensor (SeaWiFS), coastal zone color scanner (CZCS), ocean and land colour instrument (OLCI), and visible infrared imaging radiometer suite (VIIRS) sensors. Results are also shown for CIMEL’s SeaPRISM sun photometer sensor used on-board field trips. Accuracy of more than 92% is observed on the validation dataset and more than 86% is observed on the other dataset for all satellite sensors. The potential of applying the algorithms to non-satellite and non-multi-spectral sensors mountable on airborne systems is demonstrated by showing classification results for two consumer cameras. Classification on actual MERIS data is also shown. Additional results comparing the spectra of remote sensing reflectance with level 2 MERIS data and chlorophyll concentration estimates of the data are included.http://www.mdpi.com/1424-8220/16/3/413spectral data classificationenvironmental sensorsocean colorremote sensing reflectance
spellingShingle Dilip K. Prasad
Krishna Agarwal
Classification of Hyperspectral or Trichromatic Measurements of Ocean Color Data into Spectral Classes
Sensors
spectral data classification
environmental sensors
ocean color
remote sensing reflectance
title Classification of Hyperspectral or Trichromatic Measurements of Ocean Color Data into Spectral Classes
title_full Classification of Hyperspectral or Trichromatic Measurements of Ocean Color Data into Spectral Classes
title_fullStr Classification of Hyperspectral or Trichromatic Measurements of Ocean Color Data into Spectral Classes
title_full_unstemmed Classification of Hyperspectral or Trichromatic Measurements of Ocean Color Data into Spectral Classes
title_short Classification of Hyperspectral or Trichromatic Measurements of Ocean Color Data into Spectral Classes
title_sort classification of hyperspectral or trichromatic measurements of ocean color data into spectral classes
topic spectral data classification
environmental sensors
ocean color
remote sensing reflectance
url http://www.mdpi.com/1424-8220/16/3/413
work_keys_str_mv AT dilipkprasad classificationofhyperspectralortrichromaticmeasurementsofoceancolordataintospectralclasses
AT krishnaagarwal classificationofhyperspectralortrichromaticmeasurementsofoceancolordataintospectralclasses