A Hybrid Convolutional Neural Network and Random Forest for Burned Area Identification with Optical and Synthetic Aperture Radar (SAR) Data
Abstract
:1. Introduction
2. Materials and Methods
2.1. Research Area and Data
2.2. Methodology
2.3. Convolutional Neural Network (CNN)
2.4. Random Forest (RF)
2.5. Proposed Method
2.6. Training and Validation Dataset
2.7. Classifiers Implementation
2.8. Classification Scheme
2.9. Evaluation Parameter
3. Results
3.1. Hotspot Distribution Pattern
3.2. Classification Result of Burned Area using CNN-RF Method
4. Discussion
4.1. Comparison among CNN-RF, CNN, RF, and NN Methods
4.2. Burned Area Estimation
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Sensor | Specifications | |||
---|---|---|---|---|
Sentinel-2 MSI | Date | Pre-fire: July 2019 | Post-fire: October 2019 | |
Bands | Band number | Band name | Resolution | |
B2 | Blue | 10 | ||
B3 | Green | 10 | ||
B4 | Red | 10 | ||
B5 | Vegetation red edge 1 | 20 | ||
B6 | Vegetation red edge 2 | 20 | ||
B7 | Vegetation red edge 3 | 20 | ||
B8 | NIR | 10 | ||
B8A | Narrow NIR | 20 | ||
B11 | SWIR1 | 20 | ||
B12 | SWIR2 | 20 | ||
Product Level | L-2A | |||
Sentinel-1 (C-Band SAR) | Date | Pre-fire: July 2019 | Post-fire: October 2019 | |
Frequency | 5.405 GHz | |||
Orbit | Descending | |||
Product Type | Ground range detected | |||
Acquisition Mode | Interferometric wide swath | |||
Polarization Mode | VV and VH |
Scheme | Sensor | Band/Polarization |
---|---|---|
#1 | Optical | 20 bands of pre-fire and post-fire events |
# 2 | SAR | 8 bands VV and VH polarization of and of pre-fire and post-fire events |
# 3 | Optical and SAR VH Polarization | optical band and VH polarization of and of pre-fire and post-fire events, with a total of 24 bands |
# 4 | Optical and SAR VV Polarization | optical band and VV polarization of and of pre-fire and post-fire events, with a total of 24 bands |
# 5 | Optical and SAR VH and VV Polarization | optical band and VH and VV polarization of and of pre-fire and post-fire events, with a total of 28 bands |
Sensor | Scheme | CNN | ||||
OA | Recall | Precision | F1 -Score | K | ||
Optical | #1 | 0.9619 | 0.9475 | 0.9767 | 0.9619 | 0.9237 |
SAR | #2 | 0.7136 | 0.7553 | 0.7030 | 0.7282 | 0.4263 |
Optical and SAR | #3 | 0.9676 | 0.9665 | 0.9697 | 0.9681 | 0.9352 |
#4 | 0.9716 | 0.9793 | 0.9653 | 0.9723 | 0.9432 | |
#5 | 0.9682 | 0.9519 | 0.9849 | 0.9681 | 0.9363 | |
RF | ||||||
Optical | #1 | 0.9580 | 0.9427 | 0.9738 | 0.9580 | 0.9160 |
SAR | #2 | 0.6991 | 0.6908 | 0.7094 | 0.7000 | 0.3983 |
Optical and SAR | #3 | 0.9621 | 0.9498 | 0.9749 | 0.9622 | 0.9241 |
#4 | 0.9635 | 0.9513 | 0.9763 | 0.9636 | 0.9271 | |
#5 | 0.9636 | 0.9525 | 0.9753 | 0.9638 | 0.9272 | |
NN | ||||||
Optical | #1 | 0.9611 | 0.9602 | 0.9632 | 0.9617 | 0.9223 |
SAR | #2 | 0.6953 | 0.6614 | 0.7170 | 0.6881 | 0.3912 |
Optical and SAR | #3 | 0.9548 | 0.9742 | 0.9391 | 0.9563 | 0.9095 |
#4 | 0.9506 | 0.9483 | 0.9543 | 0.9512 | 0.9012 | |
#5 | 0.9580 | 0.9593 | 0.9580 | 0.9587 | 0.9159 | |
CNN-RF | ||||||
Optical | #1 | 0.9644 | 0.9520 | 0.9773 | 0.9645 | 0.9287 |
SAR | #2 | 0.7029 | 0.6990 | 0.7113 | 0.7051 | 0.4058 |
Optical and SAR | #3 | 0.9719 | 0.9660 | 0.9783 | 0.9721 | 0.9437 |
#4 | 0.9718 | 0.9641 | 0.9800 | 0.9720 | 0.9435 | |
#5 | 0.9725 | 0.9618 | 0.9837 | 0.9726 | 0.9450 |
Scheme | Burned Area Estimation (Hectares) | |||
---|---|---|---|---|
CNN | RF | NN | CNN-RF | |
#1 | 47,834.74 | 47,762.48 | 51,749.67 | 48,367.02 |
#2 | 112,691.83 | 108,029.40 | 96,339.55 | 106,945.36 |
#3 | 51,750.16 | 47,536.03 | 55,671.40 | 48,735.11 |
#4 | 50,428.16 | 47,659.81 | 51,366.37 | 48,514.23 |
#5 | 47,903.56 | 47,433.93 | 50,783.50 | 48,824.59 |
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Sudiana, D.; Lestari, A.I.; Riyanto, I.; Rizkinia, M.; Arief, R.; Prabuwono, A.S.; Sri Sumantyo, J.T. A Hybrid Convolutional Neural Network and Random Forest for Burned Area Identification with Optical and Synthetic Aperture Radar (SAR) Data. Remote Sens. 2023, 15, 728. https://doi.org/10.3390/rs15030728
Sudiana D, Lestari AI, Riyanto I, Rizkinia M, Arief R, Prabuwono AS, Sri Sumantyo JT. A Hybrid Convolutional Neural Network and Random Forest for Burned Area Identification with Optical and Synthetic Aperture Radar (SAR) Data. Remote Sensing. 2023; 15(3):728. https://doi.org/10.3390/rs15030728
Chicago/Turabian StyleSudiana, Dodi, Anugrah Indah Lestari, Indra Riyanto, Mia Rizkinia, Rahmat Arief, Anton Satria Prabuwono, and Josaphat Tetuko Sri Sumantyo. 2023. "A Hybrid Convolutional Neural Network and Random Forest for Burned Area Identification with Optical and Synthetic Aperture Radar (SAR) Data" Remote Sensing 15, no. 3: 728. https://doi.org/10.3390/rs15030728