Next Article in Journal
Comparison of Computer Vision and Photogrammetric Approaches for Epipolar Resampling of Image Sequence
Previous Article in Journal
An Innovative Optical Sensor for the Online Monitoring and Control of Biomass Concentration in a Membrane Bioreactor System for Lactic Acid Production
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(3), 413; doi:10.3390/s16030413

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

1
School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore
2
Singapore-MIT Alliance for Research and Technology, Singapore 138602, Singapore
*
Author to whom correspondence should be addressed.
Academic Editor: Assefa M. Melesse
Received: 22 December 2015 / Revised: 14 March 2016 / Accepted: 17 March 2016 / Published: 22 March 2016
(This article belongs to the Section Remote Sensors)
View Full-Text   |   Download PDF [12895 KB, uploaded 22 March 2016]   |  

Abstract

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. View Full-Text
Keywords: spectral data classification; environmental sensors; ocean color; remote sensing reflectance spectral data classification; environmental sensors; ocean color; remote sensing reflectance
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Prasad, D.K.; Agarwal, K. Classification of Hyperspectral or Trichromatic Measurements of Ocean Color Data into Spectral Classes. Sensors 2016, 16, 413.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top