An Improved Unmixing-Based Fusion Method: Potential Application to Remote Monitoring of Inland Waters
<p>Location of the study area.</p> "> Figure 2
<p>Schema of the improved methodology.</p> "> Figure 3
<p>Part of the georeferenced MERIS image band 13 (<b>a</b>), and RGB colour composite of bands 4, 3, and 2 of the HJ1-CCD image overlaying RGB colour composite of bands 13; 7, and 5 of the MERIS image. Note that the MERIS image is shown as a semi-transparent background in (<b>b</b>). The scatter plots show the relationship between HJ1-CCD image band 4 and MERIS image band 13 of the body of water at 300-m spatial scale (<b>c</b>).</p> "> Figure 4
<p>ERGAS results as a function of <span class="html-italic">w</span> (window size), <span class="html-italic">K</span> (number of classes) and <span class="html-italic">α</span> (regularisation parameter).</p> "> Figure 5
<p>ERGAS<sub>300</sub> value of each UBF and IUBF production band in dataset #1.</p> "> Figure 6
<p>RGB colour composite of (<b>a</b>) bands 4, 3, and 2 of the HJ1-CCD image; (<b>b</b>) bands 13, 7, and 5 of the MERIS image; (<b>c</b>) bands 13, 7, and 5 of the fused image processed with the UBF algorithm; (<b>d</b>) bands 13, 7, and 5 of the fused image processed with the IUBF algorithm; (<b>e</b>) subset of the HJ1-CCD image in area C; (<b>f</b>) subset of the MERIS image in area C; (<b>g</b>) subset of the UBF fusion in area C; (<b>h</b>) subset of the IUBF fusion in area C; (<b>i</b>) subset of the HJ1-CCD image in area D; (<b>j</b>) subset of the MERIS image in area D; (<b>k</b>) subset of the UBF fusion in area D; (<b>l</b>) subset of the IUBF fusion in area D.</p> "> Figure 6 Cont.
<p>RGB colour composite of (<b>a</b>) bands 4, 3, and 2 of the HJ1-CCD image; (<b>b</b>) bands 13, 7, and 5 of the MERIS image; (<b>c</b>) bands 13, 7, and 5 of the fused image processed with the UBF algorithm; (<b>d</b>) bands 13, 7, and 5 of the fused image processed with the IUBF algorithm; (<b>e</b>) subset of the HJ1-CCD image in area C; (<b>f</b>) subset of the MERIS image in area C; (<b>g</b>) subset of the UBF fusion in area C; (<b>h</b>) subset of the IUBF fusion in area C; (<b>i</b>) subset of the HJ1-CCD image in area D; (<b>j</b>) subset of the MERIS image in area D; (<b>k</b>) subset of the UBF fusion in area D; (<b>l</b>) subset of the IUBF fusion in area D.</p> "> Figure 7
<p>Pixel values on line AB of (<b>a</b>) HJ1-CCD image band 1, MERIS image and fused image band 2; (<b>b</b>) HJ1-CCD image band 2, MERIS image and fused image band 5; (<b>c</b>) HJ1-CCD image band 3, MERIS image and fused image band 7; (<b>d</b>) HJ1-CCD image band 4, MERIS image and fused image band 13.</p> "> Figure 8
<p>Spectra of the 12 points on the HJ1-CCD image, MERIS image, UBF fused image and IUBF fused image.</p> "> Figure 8 Cont.
<p>Spectra of the 12 points on the HJ1-CCD image, MERIS image, UBF fused image and IUBF fused image.</p> "> Figure 9
<p>ERGAS<sub>300</sub> value of each UBF and IUBF production band in dataset #2.</p> "> Figure 10
<p>Mapping of <span class="html-italic">K</span><sub>c</sub> for (<b>a</b>) IUBF in dataset #1; (<b>b</b>) UBF in dataset #1; (<b>c</b>) IUBF in dataset#2; (<b>d</b>) UBF in dataset #2; and the RGB color composite of bands 4, 3, and 2 of the HJ1-CCD image with the sampling stations distribution (<b>e</b>).</p> "> Figure 11
<p>Mapping of S<sub>13</sub>/I<sub>13</sub>, in dataset #1 (<b>a</b>) and dataset #2 (<b>b</b>).</p> "> Figure 12
<p>Scatter plot of relationships between the measured <span class="html-italic">C</span><sub>chla</sub> and the estimated <span class="html-italic">C</span><sub>chla</sub> by the (<b>a</b>) MERIS image; (<b>b</b>) IUBF fused image; (<b>c</b>) UBF fused image.</p> "> Figure 13
<p>Mapping of the <span class="html-italic">C</span><sub>Chla</sub> distribution for Lake Taihu with the (<b>a</b>) MERIS image; (<b>b</b>) UBF fused image; (<b>c</b>) IUBF fused image in dataset #2; and the probability density plots of <span class="html-italic">C</span><sub>chla</sub> in the black rectangle (<b>d</b>). (Rivers names: 1. Chendonggang River, 2. Guandugang River, 3. Shatanggang River, 4. Taige Channel, 5. Caoqiao River, 6. Wujingang River, 7. Zhihugang River, 8. Wangyu River, 9. Huguang River, 10. Xujiang River, 11. Taipu River, 12. Tiaoxi River, 13. Changxinggang River).</p> ">
Abstract
:1. Introduction
2. Materials
2.1. Study Area
2.2. Datasets
Dataset | Date | Image Data | Sensing Time Difference | Usage |
---|---|---|---|---|
#1 | 5 October 2010 | HJ1-CCD image; MERIS image | 6 min | Algorithm validation and parameter optimisation |
#2 | 9 August 2010 | HJ1-CCD image; MERIS image; | 7 min | Algorithm application |
Specifications | HJ1-CCD | MERIS | ||
---|---|---|---|---|
Swath width(km) | 360 | 1150 | ||
Altitude(km) | 750 | 800 | ||
Spatial resolution(m) | 30 | 1200/300 | ||
Revisit time(days) | 2 | 3 | ||
Quantitative value(bit) | 8 | 16 | ||
Average Signal to noise ratio(dB) | ≥48 | 1650 | ||
Band setting | Centre(nm) | Width(nm) | Centre(nm) | Width(nm) |
412.5 | 10 | |||
475 | 90 | 442.5 | 10 | |
490 | 10 | |||
510 | 10 | |||
560 | 80 | 560 | 10 | |
660 | 60 | 620 | 10 | |
665 | 10 | |||
681.25 | 7.5 | |||
708.75 | 10 | |||
753.75 | 7.5 | |||
830 | 140 | 761.875 | 3.75 | |
778.75 | 15 | |||
865 | 20 |
3. Methods
3.1. The Unmixing-Based Fusion (UBF) Method
3.2. Methodology Improvement
4. Results
4.1. Co-Registration Accuracy
4.2. Algorithm Validation
4.2.1. HJ1-CCD Band Selection
MERIS Band | HJ1-CCD Band | R | MERIS Band | HJ1-CCD Band | R |
---|---|---|---|---|---|
1 | 1 | 0.873 | 9 | 4 | 0.852 |
2 | 1 | 0.937 | 10 | 4 | 0.948 |
3 | 1 | 0.949 | 11 | 4 | 0.947 |
4 | 1 | 0.942 | 12 | 4 | 0.946 |
5 | 2 | 0.913 | 13 | 4 | 0.944 |
6 | 1 | 0.942 | 14 | 4 | 0.939 |
7 | 3 | 0.944 | 15 | 4 | 0.938 |
8 | 3 | 0.928 |
4.2.2. Free Parameter Optimisation
w | α | K | ERGAS300 | ERGAS30 | |
---|---|---|---|---|---|
UBF | 7 | 0.1 | 40 | 0.348 | 0.440 |
IUBF | 7 | 0.001 | - | 0.232 | 0.497 |
4.2.3. Interpolation Contribution
4.3. Evaluation of the Fusion Effect
4.3.1. Visual Effects
4.3.2. Radiance Changes
4.3.3. Spectrum Shape
5. Discussion
5.1. Robustness of the IUBF Algorithm
w | α | K | ERGAS300 | ERGAS30 | |
---|---|---|---|---|---|
UBF | 7 | 0.1 | 40 | 0.447 | 1.301 |
IUBF | 7 | 0.001 | - | 0.349 | 1.291 |
5.2. Interpolation Quality in the Spatial Scale
5.3. Potential Application of Remote Water Monitoring
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Guo, Y.; Li, Y.; Zhu, L.; Liu, G.; Wang, S.; Du, C. An Improved Unmixing-Based Fusion Method: Potential Application to Remote Monitoring of Inland Waters. Remote Sens. 2015, 7, 1640-1666. https://doi.org/10.3390/rs70201640
Guo Y, Li Y, Zhu L, Liu G, Wang S, Du C. An Improved Unmixing-Based Fusion Method: Potential Application to Remote Monitoring of Inland Waters. Remote Sensing. 2015; 7(2):1640-1666. https://doi.org/10.3390/rs70201640
Chicago/Turabian StyleGuo, Yulong, Yunmei Li, Li Zhu, Ge Liu, Shuai Wang, and Chenggong Du. 2015. "An Improved Unmixing-Based Fusion Method: Potential Application to Remote Monitoring of Inland Waters" Remote Sensing 7, no. 2: 1640-1666. https://doi.org/10.3390/rs70201640