Lake Ice-Water Classification of RADARSAT-2 Images by Integrating IRGS Segmentation with Pixel-Based Random Forest Labeling
Abstract
:1. Introduction
2. Data
2.1. Synthetic Aperture Radar Data
2.2. Reference Data
3. Methodology
3.1. IRGS Segmentation
3.2. IRGS-Manual Semi-Automated Classification
3.3. Automated Classification
3.3.1. Features
- Contrast Group: contrast (CON), dissimilarity (DIS), homogeneity (HOM)
- Orderliness Group: applied second moment (ASM), entropy (ENT), inverse moment (INV)
- Statistics Group: mean (MU), standard deviation (STD), correlation (COR)
3.3.2. Classifiers
Support Vector Machine
Random Forest
3.4. Integration of Segmentation and Labeling
4. Results
5. Discussion
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A. Software and Hardware Specifications
Appendix A.1. Software
Appendix A.2. Hardware
- For IRGS-Manual classification: Intel i7 4790 with 16 GB RAM
- For RF, SVM, IRGS-RF and IRGS-SVM: Intel i7 6700K with 32 GB RAM
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Scene ID | SAR Acquisition Date (M/D/Y) | Acquisition Time UTC (hh:mm:ss) | Ascending (A)/ Descending (D) |
---|---|---|---|
20131122_142937 | 11/22/2013 | 14:29:37 | D |
20131129_142532 | 11/29/2013 | 14:25:32 | D |
20131205_145035 | 12/05/2013 | 14:50:35 | D |
20131206_142120 | 12/06/2013 | 14:21:21 | D |
20131212_144618 | 12/12/2013 | 14:46:18 | D |
20131219_144152 | 12/19/2013 | 14:41:52 | D |
20140612_143755 | 06/12/2014 | 14:37:55 | D |
20140619_143342 | 06/19/2014 | 14:33:42 | D |
20140629_144205 | 06/19/2014 | 14:42:05 | D |
20140703_142514 | 07/03/2014 | 14:25:14 | D |
20140710_142114 | 07/10/2014 | 14:21:14 | D |
20141031_142522 | 10/31/2014 | 14:25:22 | D |
20141106_145027 | 11/06/2014 | 14:50:27 | D |
20141107_142115 | 11/07/2014 | 14:21:15 | D |
20141112_013553 | 11/12/2014 | 01:35:53 | A |
20141120_144206 | 11/20/2014 | 14:42:06 | D |
20141127_143745 | 11/27/2014 | 14:37:45 | D |
20141203_012332 | 12/03/2014 | 01:23:32 | A |
20150528_142923 | 05/28/2015 | 14:29:23 | D |
20150606_012716 | 06/06/2015 | 01:27:16 | A |
20150611_142104 | 06/11/2015 | 14:21:04 | D |
20150618_141641 | 06/18/2015 | 14:16:41 | D |
20150701_143743 | 07/01/2015 | 14:37:43 | D |
20151029_143745 | 10/29/2015 | 14:37:45 | D |
20151105_143336 | 11/05/2015 | 14:33:36 | D |
20151112_142917 | 11/12/2015 | 14:29:17 | D |
20151119_142512 | 11/19/2015 | 14:25:12 | D |
20151126_142102 | 11/26/2015 | 14:21:02 | D |
20151202_144602 | 12/02/2015 | 14:46:02 | D |
20151209_144152 | 12/09/2015 | 14:41:52 | D |
20160518_144544 | 05/18/2016 | 14:45:44 | D |
20160525_144140 | 05/25/2016 | 14:41:40 | D |
20160610_013545 | 06/10/2016 | 01:35:45 | A |
20160617_013133 | 06/17/2016 | 01:31:33 | A |
20160630_015219 | 06/30/2016 | 01:52:19 | A |
20160706_141626 | 07/06/2016 | 14:16:26 | D |
Window Size (Pixels) | Step Size (Pixels) |
---|---|
5 by 5 | 1 |
11 by 11 | 1 |
25 by 25 | 1 |
25 by 25 | 5 |
51 by 51 | 5 |
51 by 51 | 10 |
51 by 51 | 20 |
101 by 101 | 10 |
101 by 101 | 20 |
Feature Rank | Polarization | Feature Name | Window Size | Step Size |
---|---|---|---|---|
1 | HV | GLCM ASM | 25 by 25 | 1 |
2 | HV | GLCM COR | 25 by 25 | 1 |
3 | HV | GLCM ASM | 25 by 25 | 5 |
4 | HV | GLCM ENT | 11 by 11 | 1 |
5 | HV | GLCM ASM | 11 by 11 | 1 |
6 | HH | GLCM HOM | 101 by 101 | 10 |
7 | HH | GLCM ASM | 11 by 11 | 1 |
8 | HH | GLCM MU | 51 by 51 | 20 |
9 | HV | GLCM ASM | 51 by 51 | 5 |
10 | HH | GLCM COR | 25 by 25 | 1 |
11 | HH | GLCM ASM | 25 by 25 | 1 |
12 | HV | GLCM STD | 25 by 25 | 5 |
13 | HV | GLCM STD | 11 by 11 | 1 |
14 | HH | GLCM INV | 101 by 101 | 10 |
15 | HH | GLCM MU | 51 by 15 | 10 |
16 | HH | GLCM HOM | 51 by 51 | 5 |
17 | HH | GLCM MU | 51 by 51 | 5 |
18 | HV | GLCM ASM | 51 by 51 | 10 |
19 | HV | GLCM HOM | 25 by 25 | 5 |
20 | HH | GLCM ASM | 25 by 25 | 5 |
21 | HH | GLCM MU | 101 by 101 | 10 |
22 | HH | GLCM ASM | 5 by 5 | 1 |
23 | HH | GLCM HOM | 101 by 101 | 20 |
24 | HH | GLCM COR | 11 by 11 | 1 |
25 | HH | GLCM MU | 5 by 5 | 1 |
26 | HH | GLCM HOM | 25 by 25 | 1 |
27 | HH | Pixel Average | 25 by 25 | 1 |
28 | HV | GLCM ASM | 101 by 101 | 10 |
29 | HH | GLCM ASM | 51 by 51 | 5 |
30 | HV | GLCM INV | 25 by 25 | 5 |
31 | HH | Pixel Average | 5 by 5 | 1 |
Scene ID | IRGS-Manual | SVM | RF | IRGS-SVM | IRGS-RF |
---|---|---|---|---|---|
20131122_142937 | 81.9 | 68.3 | 87.8 | 83.5 | 88.8 |
20131129_142532 | 86.8 | 63.3 | 87.8 | 61.9 | 89.7 |
20131205_145035 | 98.8 | 85.8 | 97.8 | 98.5 | 98.0 |
20131206_142120 | 97.5 | 90.0 | 97.3 | 97.7 | 97.7 |
20131212_144618 | 100.0 | 91.8 | 97.3 | 99.3 | 100.0 |
20131219_144152 | 100.0 | 91.3 | 96.8 | 100.0 | 100.0 |
20140612_143755 | 99.5 | 90.5 | 98.8 | 99.5 | 99.5 |
20140619_143342 | 98.5 | 87.8 | 98.8 | 95.8 | 99.3 |
20140629_144205 | 92.3 | 68.8 | 85.8 | 64.3 | 85.0 |
20140703_142514 | 93.3 | 76.5 | 90.5 | 90.0 | 92.8 |
20140710_142114 | 99.2 | 78.0 | 99.2 | 87.4 | 99.7 |
20141031_142522 | 98.3 | 61.5 | 93.8 | 89.0 | 97.5 |
20141106_145027 | 92.0 | 33.5 | 95.8 | 27.3 | 95.0 |
20141107_142115 | 86.1 | 55.8 | 91.5 | 61.5 | 90.0 |
20141112_013553 | 88.5 | 54.0 | 93.8 | 54.3 | 93.8 |
20141120_144206 | 84.8 | 83.8 | 94.50 | 85.5 | 92.5 |
20141127_143745 | 86.0 | 81.0 | 94.3 | 84.8 | 98.5 |
20141203_012332 | 93.0 | 92.8 | 98.4 | 99.7 | 99.5 |
20150528_142923 | 98.8 | 88.6 | 99.5 | 100.0 | 100.0 |
20150606_012716 | 99.7 | 81.8 | 90.0 | 84.4 | 99.5 |
20150611_142104 | 98.5 | 97.5 | 99.3 | 97.8 | 99.5 |
20150618_141641 | 99.5 | 94.5 | 98.3 | 93.5 | 98.8 |
20150701_143743 | 87.5 | 10.3 | 89.0 | 3.8 | 92.8 |
20151029_143745 | 96.5 | 58.0 | 94.5 | 60.8 | 97.5 |
20151105_143336 | 96.0 | 74.0 | 92.5 | 80.1 | 95.2 |
20151112_142917 | 88.3 | 73.3 | 90.3 | 92.0 | 91.0 |
20151119_142512 | 88.3 | 75.8 | 87.3 | 90.5 | 93.8 |
20151126_142102 | 77.3 | 77.3 | 93.3 | 91.6 | 91.0 |
20151202_144602 | 84.0 | 82.8 | 98.0 | 96.3 | 98.8 |
20151209_144152 | 100.0 | 89.5 | 98.0 | 100.0 | 100.0 |
20160518_144544 | 100.0 | 84.3 | 72.3 | 99.0 | 95.5 |
20160525_144140 | 100.0 | 76.5 | 70.0 | 78.3 | 88.3 |
20160610_013545 | 97.5 | 87.8 | 99.0 | 91.2 | 98.5 |
20160617_013133 | 95.0 | 79.3 | 94.5 | 75.0 | 94.8 |
20160630_015219 | 93.3 | 28.0 | 96.8 | 27.5 | 99.0 |
20160706_141626 | 98.8 | 77.0 | 97.8 | 76.8 | 99.0 |
Overall Accuracy | 93.8 | 74.7 | 93.3 | 81.1 | 95.8 |
Ice Error | 2.6 | 6.3 | 3.3 | 2.3 | 1.9 |
Open Water Error | 3.7 | 19.0 | 3.5 | 16.7 | 2.2 |
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Share and Cite
Hoekstra, M.; Jiang, M.; Clausi, D.A.; Duguay, C. Lake Ice-Water Classification of RADARSAT-2 Images by Integrating IRGS Segmentation with Pixel-Based Random Forest Labeling. Remote Sens. 2020, 12, 1425. https://doi.org/10.3390/rs12091425
Hoekstra M, Jiang M, Clausi DA, Duguay C. Lake Ice-Water Classification of RADARSAT-2 Images by Integrating IRGS Segmentation with Pixel-Based Random Forest Labeling. Remote Sensing. 2020; 12(9):1425. https://doi.org/10.3390/rs12091425
Chicago/Turabian StyleHoekstra, Marie, Mingzhe Jiang, David A. Clausi, and Claude Duguay. 2020. "Lake Ice-Water Classification of RADARSAT-2 Images by Integrating IRGS Segmentation with Pixel-Based Random Forest Labeling" Remote Sensing 12, no. 9: 1425. https://doi.org/10.3390/rs12091425
APA StyleHoekstra, M., Jiang, M., Clausi, D. A., & Duguay, C. (2020). Lake Ice-Water Classification of RADARSAT-2 Images by Integrating IRGS Segmentation with Pixel-Based Random Forest Labeling. Remote Sensing, 12(9), 1425. https://doi.org/10.3390/rs12091425