Classification of Hyperspectral or Trichromatic Measurements of Ocean Color Data into Spectral Classes
Abstract
:1. Introduction
- without requiring atmospheric correction for satellite sensors, despite the contribution of the remote sensing reflectance being only a small part of the TOA radiances, or
- without requiring mutlispectral sensors, such that airborne broad band sensors such as consumer cameras can be used.
2. Information about Datasets and Sensors for Synthetic Experiments
3. Proposed Classification Approach
3.1. Radiometric Measurements as Functions of Remote Sensing Reflectance Spectra
3.1.1. Satellite Sensors
3.1.2. Airborne Sensors
3.2. Classification Approach
3.3. An Example of Finding Characteristic Spectra for the Lookup Table
4. Results
4.1. Synthetic Experiments
4.1.1. Classification Results for Dataset 1
4.1.2. Classification Results for Dataset 2
4.1.3. Different Illuminations and Classification Accuracy
4.1.4. Classification Using Consumer Cameras
4.2. Classification of Real Satellite Data
4.2.1. Classification of MERIS Data over Different Scenes
4.2.2. Correlation with IOP Results in the Same Data
4.2.3. Similarity between Estimated Remote Sensing Reflectances Using MERIS Level 2 Data and the Proposed Method
4.2.4. Effect of the Value β on Classification
4.2.5. Assumption of Local Uniformity
4.2.6. Consideration of Glint
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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MODIS | MERIS | SeaWiFS | CZCS | OLCI | VIIRS (VisNIR) | CIMEL SeaPRISM | |
---|---|---|---|---|---|---|---|
No. of bands | 9 | 11 | 8 | 4 | 21 | 9 | 9 |
No. used (B) | 8 | 11 | 7 | 4 | 15 | 7 | 6 |
Bands | 405–420 | 405.2–419.6 | 402–422 | 425–460 | 407.5–417.5 | 402–422 | 407–417 |
used (nm) | 438–448 | 435.0–449.5 | 433–453 | 500–535 | 437.5–447.5 | 436–454 | 435–445 |
483–493 | 482.3–496.9 | 480–500 | 535–565 | 485–495 | 478–498 | 495–505 | |
526–536 | 502.2–516.8 | 500–520 | 650–685 | 505–515 | 545–565 | 526–536 | |
546–556 | 552.1–566.7 | 545–565 | 555–565 | 600–680 | 545–555 | ||
662–672 | 612.0–626.6 | 660–680 | 615–625 | 662–682 | 670–680 | ||
673–683 | 657.0–671.6 | 745–785 | 660–670 | 739–754 | |||
743–753 | 674.5–686.6 | 670–677.5 | |||||
700.7–715.3 | 677.5–685 | ||||||
747.0–759.0 | 703.75–713.75 | ||||||
755.8–764.1 | 750–757.5 | ||||||
760–762.5 | |||||||
762.5–766.25 | |||||||
766.25–768.75 | |||||||
771.25–786.25 | |||||||
Bands | 862–877 | 845–885 | 392.5–407.5 | 846–885 (I2) | |||
unused (nm) | 855–875 | 846–885 (M7) | 865–875 | ||||
880–890 | 931–941 | ||||||
895–905 | 1015–1025 | ||||||
930–950 | |||||||
1000–1040 |
Symbol | Meaning |
---|---|
n; N | index of the wavelength sample ; total number of wavelength samples |
m; M | index of the location of measurement; total number of location samples |
c; C | index of the spectral class; total number of spectral classes |
b; B | index of the channel in a sensor; total number of channels in a sensor |
Upwelling radiance measured at the sensor | |
Upwelling radiance leaving the water column | |
Upwelling radiance at radiance due to atmospheric scattering and reflection from water surface | |
, , | Upwelling radiance at sensor at sunny, shadowed, and cloud regions, respectively |
Downwelling radiance at the water column | |
Portion of corresponding to atmospheric scattering | |
; | remote sensing reflectance at wavelength λ; |
remote sensing reflectance at the mth location Equation (1) | |
normalized remote sensing reflectance at the mth location Equation (20) | |
normalized remote sensing reflectance representing the cth spectral class | |
Spectrally flat remote sensing reflectance of cloud | |
; β | Ratio ; constant approximation of |
α; | Constant ; class assigned to mth data point by our algorithm using Equation (19) |
sensor’s spectral response matrix given as | |
spectral sensitivity of the bth channel of sensor given as | |
sensor’s radiometric measurement (data) given as | |
sensor’s white data computed differently for satellite and airborne sensors | |
canonical class representative (CCR) of the cth class stored in the lookup table | |
canonical data obtained using data and in Equation (14) | |
canonical normalized data (CND) computed using Equation (16) |
Sensor | Measure | Class 1 | Class 2 | Class 3 | Class 4 | Class 5 | Class 6 | Class 7 | Class 8 | Overall |
---|---|---|---|---|---|---|---|---|---|---|
MODIS | Precision | 0.8889 | 0.8810 | 1.0000 | 0.9545 | 0.9667 | 0.9706 | 0.9636 | 0.9744 | − |
Recall | 0.9231 | 0.9487 | 0.9143 | 1.0000 | 0.9667 | 1.0000 | 0.8983 | 1.0000 | 0.9502 | |
MERIS | Precision | 0.9630 | 0.9286 | 1.0000 | 1.0000 | 0.9655 | 0.9706 | 1.0000 | 0.9737 | − |
Recall | 1.0000 | 1.0000 | 0.9429 | 1.0000 | 0.9333 | 1.0000 | 0.9661 | 0.9737 | 0.9751 | |
SeaWiFS | Precision | 0.8214 | 0.8372 | 1.0000 | 0.9545 | 0.9375 | 0.9412 | 0.9434 | 1.0000 | − |
Recall | 0.8846 | 0.9231 | 0.8857 | 1.0000 | 1.0000 | 0.9697 | 0.8475 | 1.0000 | 0.9288 | |
CZCS | Precision | 0.8065 | 0.8372 | 1.0000 | 1.0000 | 0.9355 | 0.9394 | 0.9455 | 0.9744 | − |
Recall | 0.9615 | 0.9231 | 0.8857 | 0.8571 | 0.9667 | 0.9394 | 0.8814 | 1.0000 | 0.9253 | |
OLCI | Precision | 0.8667 | 0.8864 | 1.0000 | 1.0000 | 0.9333 | 0.9706 | 1.0000 | 0.9730 | − |
Recall | 1.0000 | 1.0000 | 0.9143 | 0.9048 | 0.9333 | 1.0000 | 0.9322 | 0.9474 | 0.9502 | |
VIIRS | Precision | 0.9259 | 0.9750 | 1.0000 | 0.9545 | 0.9375 | 1.0000 | 1.0000 | 1.0000 | − |
Recall | 0.9615 | 1.0000 | 0.9429 | 1.0000 | 1.0000 | 1.0000 | 0.9661 | 0.9737 | 0.9786 | |
SeaPRISM | Precision | 0.7931 | 0.8205 | 1.0000 | 0.9545 | 0.9355 | 0.8421 | 0.9245 | 0.9737 | − |
Recall | 0.8846 | 0.8205 | 0.8857 | 1.0000 | 0.9667 | 0.9697 | 0.8305 | 0.9737 | 0.9039 | |
Canon | Precision | 0.8889 | 0.6667 | 0.6923 | 1.0000 | 0.7895 | 0.9167 | 0.9483 | 1.0000 | − |
Recall | 0.9231 | 0.7179 | 0.7714 | 0.8571 | 1.0000 | 0.6667 | 0.9322 | 0.9211 | 0.8505 | |
Nikon | Precision | 0.8929 | 0.7632 | 0.7027 | 1.0000 | 0.7692 | 0.9310 | 0.9655 | 1.0000 | − |
Recall | 0.9615 | 0.7436 | 0.7429 | 0.8571 | 1.0000 | 0.8182 | 0.9492 | 0.8947 | 0.8719 |
Sensor | Measure | Class 1 | Class 4 | Class 6 | Class 8 | Overall |
---|---|---|---|---|---|---|
MODIS | Precision | 0.8000 | 1.0000 | 1.0000 | 0.8438 | − |
Recall | 1.0000 | 0.9507 | 0.9040 | 1.0000 | 0.9468 | |
MERIS | Precision | 1.0000 | 1.0000 | 0.8986 | 0.9773 | − |
Recall | 0.9167 | 1.0000 | 0.9920 | 0.7963 | 0.9580 | |
SeaWiFS | Precision | 0.6122 | 1.0000 | 0.9904 | 0.7297 | − |
Recall | 0.8333 | 0.8662 | 0.8240 | 1.0000 | 0.8683 | |
CZCS | Precision | 0.9286 | 0.9929 | 1.0000 | 0.7941 | − |
Recall | 0.7222 | 0.9859 | 0.8640 | 1.0000 | 0.9188 | |
OLCI | Precision | 1.0000 | 0.9861 | 0.8052 | 0.9737 | − |
Recall | 0.5833 | 1.0000 | 0.9920 | 0.6852 | 0.9468 | |
VIIRS | Precision | 0.8182 | 1.0000 | 1.0000 | 0.9000 | − |
Recall | 1.0000 | 0.9648 | 0.9280 | 1.0000 | 0.9608 | |
SeaPRISM | Precision | 0.5294 | 1.0000 | 1.0000 | 0.6341 | − |
Recall | 0.5000 | 0.8873 | 0.7200 | 0.9630 | 0.8011 | |
Canon 1Ds | Precision | 0.8065 | 0.9342 | 1.0000 | 1.0000 | − |
MarkIII | Recall | 0.6944 | 1.0000 | 0.5520 | 0.6852 | 0.7647 |
Nikon D40 | Precision | 0.7576 | 0.9281 | 1.0000 | 1.0000 | − |
Recall | 0.6944 | 1.0000 | 0.6000 | 0.6852 | 0.7815 |
Dataset | MODIS | MERIS | SeaWiFS | CZCS | OLCI | VIIRS | SeaPRISM | Canon | Nikon |
---|---|---|---|---|---|---|---|---|---|
Dataset 1 | 0.9502 | 0.9749 | 0.9307 | 0.9267 | 0.9537 | 0.9751 | 0.9004 | 0.8424 | 0.8596 |
Dataset 2 | 0.9447 | 0.9581 | 0.8681 | 0.9175 | 0.9107 | 0.9580 | 0.8011 | 0.7622 | 0.7692 |
β | 0.80 | 0.85 | 0.9 |
---|---|---|---|
0.75 | 2.88 | 5.97 | 9.30 |
0.80 | 0 | 3.10 | 6.45 |
0.85 | 3.10 | 0 | 3.37 |
, | , | , | |
---|---|---|---|
Including unclassified pixels | 15.08 | 14.72 | 4.52 |
Excluding unclassified pixels | 13.06 | 12.87 | 4.06 |
© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
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Prasad, D.K.; Agarwal, K. Classification of Hyperspectral or Trichromatic Measurements of Ocean Color Data into Spectral Classes. Sensors 2016, 16, 413. https://doi.org/10.3390/s16030413
Prasad DK, Agarwal K. Classification of Hyperspectral or Trichromatic Measurements of Ocean Color Data into Spectral Classes. Sensors. 2016; 16(3):413. https://doi.org/10.3390/s16030413
Chicago/Turabian StylePrasad, Dilip K., and Krishna Agarwal. 2016. "Classification of Hyperspectral or Trichromatic Measurements of Ocean Color Data into Spectral Classes" Sensors 16, no. 3: 413. https://doi.org/10.3390/s16030413
APA StylePrasad, D. K., & Agarwal, K. (2016). Classification of Hyperspectral or Trichromatic Measurements of Ocean Color Data into Spectral Classes. Sensors, 16(3), 413. https://doi.org/10.3390/s16030413