Transferability of the Deep Learning Mask R-CNN Model for Automated Mapping of Ice-Wedge Polygons in High-Resolution Satellite and UAV Images
Abstract
:1. Introduction
2. Data and Methods
2.1. Imagery Data for Annotation
2.2. Imagery Data for Case Studies
2.3. Annotated Data for the Mask R-CNN Model
2.4. Annotated Data for Case Studies
2.5. Experimental Design
- (C1)
- We applied the Mask R-CNN model trained on VHSR fixed-wing aircraft imagery from Zhang et al. [17] to IWP mapping of a high-resolution satellite image;
- (C2)
- We applied the Mask R-CNN model trained only on high-resolution satellite imagery to IWP mapping of another high-resolution satellite image;
- (C3)
- We re-trained the model from Zhang et al. [17] with high-resolution satellite imagery and applied the model to another high-resolution satellite image;
- (C4)
- We applied the Mask R-CNN model trained only on high-resolution satellite imagery to IWP mapping of a 3-band UAV image;
- (C5)
- We applied the Mask R-CNN model trained only on VHSR fixed-wing aircraft imagery from Zhang et al. [17] to IWP mapping of the 3-band UAV image; and
- (C6)
- We re-trained the Mask R-CNN model already trained on high-resolution satellite imagery with VHSR fixed-wing aircraft imagery from Zhang et al. [17] and applied the model to the 3-band UAV image.
2.6. Quantitative Assessment
2.7. Expert-Based Qualitative Assessment
2.8. Workflow and Implementation
3. Case Studies and Results
3.1. Quantitative Assessment Based on Model Testing Datasets
3.2. Quantitative Assessment Based on Case Testing Datasets
3.2.1. C1: A Mask R-CNN Model Trained Only on VHSR Fixed-Wing Aircraft Imagery Was Applied to a High-Resolution Satellite Image
3.2.2. C2: A Mask R-CNN Model Trained Only on High-Resolution Satellite Imagery Was Applied to Another High-Resolution Satellite Image
3.2.3. C3: A Mask R-CNN Model Trained on VHSR Fixed-Wing Aircraft Imagery and Re-Trained on High-Resolution Satellite Imagery Was Applied to Another High-Resolution Satellite Image
3.2.4. C4: A Mask R-CNN Model Trained Only on High-Resolution Satellite Imagery Was Applied to a 3-Band UAV Image
3.2.5. C5: A Mask R-CNN Model Trained Only on VHSR Fixed-Wing Aircraft Imagery Was Applied to a 3-Band UAV Image
3.2.6. C6: A Mask R-CNN Model Re-Trained on High-Resolution Satellite Imagery Was Applied to a 3-Band UAV Image
4. Discussion
4.1. Effect of Spatial Resolution of Training Data on Mask R-CNN Performance
4.2. Effect of Used Spectral Bands of Training Data on Mask R-CNN Performance
4.3. Limitations of the Mask R-CNN Model
4.4. Limitations of the Annotation Data
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Category | Sensor Platform | Image ID | Acquired Date | Spatial Resolution | Used Spectral Bands | Area (sq km) | Purpose | Number of Annotated IWPs |
---|---|---|---|---|---|---|---|---|
Imagery Data for Annotation | Fixed-wing Aircraft | n/a | 09/2013 | 0.15 × 0.15 m | near-infrared, green, and blue | 42 | Training | 6022 |
Validation | 668 | |||||||
Model Testing | 798 | |||||||
WorldView-2 Satellite | 10300100065AFE00 | 07/29/2010 | 0.8 × 0.66 m | near-infrared, green, and blue | 535 | Training | 25,498 | |
Validation | 3470 | |||||||
Model Testing | 3399 | |||||||
Imagery Data for Case Studies | WorldView-2 Satellite | 10300100468D9100 | 07/07/2015 | 0.48 × 0.49 m | near-infrared, green, and blue | 272 | Case Testing | 760 |
DJI Phantom 4 UAV | n/a | 07/24/2018 | 0.02 × 0.02 m | red, green, and blue | 0.32 | Case Testing | 128 |
Case Study | Pretrained Weight Dataset | Retrained Weights Dataset | Target Imagery | Addressing Questions |
---|---|---|---|---|
C1 | VHSR fixed-wing aircraft imagery | Worldview2 imagery | Q1, Q2 | |
C2 | Worldview2 imagery | Q1, Q3 | ||
C3 | VHSR fixed-wing aircraft imagery | Worldview2 imagery | Q1, Q3 | |
C4 | Worldview2 imagery | UAV photo | Q1, Q2 | |
C5 | VHSR fixed-wing aircraft imagery | Q1, Q2 | ||
C6 | Worldview2 imagery | VHSR fixed-wing aircraft imagery | Q1, Q3 |
Case Study | The Number of Epochs in Pre-Training Processes | The Number of Epochs in Re-Training Processes |
---|---|---|
C1 | None | 8 |
C2 | None | 70 |
C3 | 8 | 55 |
C4 | None | 70 |
C5 | None | 8 |
C6 | 70 | 3 |
Case Study | Category | IoU | TP | FP | FN | Precision | Recall | F1 | AP |
---|---|---|---|---|---|---|---|---|---|
C1 and C5 | Detection | 0.5 | 596 | 132 | 202 | 0.82 | 0.75 | 78% | 0.73 |
0.75 | 519 | 209 | 279 | 0.71 | 0.65 | 68% | 0.60 | ||
Delineation | 0.5 | 591 | 137 | 207 | 0.81 | 0.74 | 77% | 0.72 | |
0.75 | 591 | 137 | 207 | 0.81 | 0.74 | 77% | 0.72 | ||
C2 and C4 | Detection | 0.5 | 2151 | 77 | 1248 | 0.97 | 0.63 | 76% | 0.66 |
0.75 | 2060 | 168 | 1339 | 0.92 | 0.61 | 73% | 0.63 | ||
Delineation | 0.5 | 2151 | 77 | 1248 | 0.97 | 0.63 | 76% | 0.66 | |
0.75 | 2151 | 77 | 1248 | 0.97 | 0.63 | 76% | 0.66 | ||
C3 | Detection | 0.5 | 2131 | 82 | 1268 | 0.96 | 0.63 | 76% | 0.66 |
0.75 | 2006 | 207 | 1393 | 0.91 | 0.59 | 71% | 0.61 | ||
Delineation | 0.5 | 2130 | 83 | 1269 | 0.96 | 0.63 | 76% | 0.66 | |
0.75 | 2130 | 83 | 1269 | 0.96 | 0.63 | 76% | 0.66 | ||
C6 | Detection | 0.5 | 618 | 160 | 180 | 0.79 | 0.77 | 78% | 0.73 |
0.75 | 534 | 244 | 264 | 0.69 | 0.67 | 68% | 0.61 | ||
Delineation | 0.5 | 617 | 161 | 181 | 0.79 | 0.77 | 78% | 0.73 | |
0.75 | 617 | 161 | 181 | 0.79 | 0.77 | 78% | 0.73 |
Case Study | Category | IoU | TP | FP | FN | Precision | Recall | F1 | AP |
---|---|---|---|---|---|---|---|---|---|
C1 | Detection | 0.5 | 300 | 43 | 460 | 0.87 | 0.39 | 54% | 0.34 |
0.75 | 243 | 100 | 517 | 0.71 | 0.32 | 44% | 0.25 | ||
Delineation | 0.5 | 298 | 45 | 462 | 0.87 | 0.39 | 54% | 0.34 | |
0.75 | 298 | 45 | 462 | 0.87 | 0.39 | 54% | 0.34 | ||
C2 | Detection | 0.5 | 583 | 269 | 177 | 0.68 | 0.77 | 72% | 0.54 |
0.75 | 480 | 372 | 280 | 0.56 | 0.63 | 60% | 0.41 | ||
Delineation | 0.5 | 587 | 265 | 173 | 0.69 | 0.77 | 73% | 0.55 | |
0.75 | 587 | 265 | 173 | 0.69 | 0.77 | 73% | 0.55 | ||
C3 | Detection | 0.5 | 602 | 307 | 158 | 0.66 | 0.79 | 72% | 0.54 |
0.75 | 507 | 402 | 253 | 0.56 | 0.67 | 61% | 0.42 | ||
Delineation | 0.5 | 601 | 308 | 159 | 0.66 | 0.79 | 72% | 0.54 | |
0.75 | 601 | 308 | 159 | 0.66 | 0.79 | 72% | 0.54 | ||
C4 | Detection | 0.5 | 70 | 30 | 58 | 0.70 | 0.55 | 61% | 0.45 |
0.75 | 51 | 49 | 77 | 0.51 | 0.40 | 45% | 0.26 | ||
Delineation | 0.5 | 69 | 31 | 59 | 0.69 | 0.54 | 61% | 0.44 | |
0.75 | 69 | 31 | 59 | 0.69 | 0.54 | 61% | 0.44 | ||
C5 | Detection | 0.5 | 71 | 27 | 57 | 0.72 | 0.55 | 63% | 0.49 |
0.75 | 61 | 37 | 67 | 0.62 | 0.48 | 54% | 0.40 | ||
Delineation | 0.5 | 71 | 27 | 57 | 0.72 | 0.55 | 63% | 0.49 | |
0.75 | 71 | 27 | 57 | 0.72 | 0.55 | 63% | 0.49 | ||
C6 | Detection | 0.5 | 87 | 34 | 41 | 0.72 | 0.68 | 70% | 0.60 |
0.75 | 72 | 49 | 56 | 0.60 | 0.56 | 58% | 0.48 | ||
Delineation | 0.5 | 85 | 36 | 43 | 0.70 | 0.66 | 68% | 0.58 | |
0.75 | 85 | 36 | 43 | 0.70 | 0.66 | 68% | 0.58 |
Average Grades of Detection | Average Grades of Delineation | |
---|---|---|
Expert 1 | 3.7 | 4.6 |
Expert 2 | 3.5 | 4.3 |
Expert 3 | 2.9 | 4.6 |
Expert 4 | 3.1 | 4.2 |
Expert 5 | 3 | 4.6 |
Expert 6 | 3.8 | 4.4 |
Average grades | 3.3 (good) | 4.5 (excellent) |
Average Grades of Detection | Average Grades of Delineation | |
---|---|---|
Expert 1 | 4 | 4.5 |
Expert 2 | 4.3 | 3 |
Expert 3 | 4.1 | 4.7 |
Expert 4 | 4.7 | 4.4 |
Expert 5 | 4 | 4 |
Expert 6 | 4.3 | 4.7 |
Average grades | 4.2 (excellent) | 4.2 (excellent) |
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Share and Cite
Zhang, W.; Liljedahl, A.K.; Kanevskiy, M.; Epstein, H.E.; Jones, B.M.; Jorgenson, M.T.; Kent, K. Transferability of the Deep Learning Mask R-CNN Model for Automated Mapping of Ice-Wedge Polygons in High-Resolution Satellite and UAV Images. Remote Sens. 2020, 12, 1085. https://doi.org/10.3390/rs12071085
Zhang W, Liljedahl AK, Kanevskiy M, Epstein HE, Jones BM, Jorgenson MT, Kent K. Transferability of the Deep Learning Mask R-CNN Model for Automated Mapping of Ice-Wedge Polygons in High-Resolution Satellite and UAV Images. Remote Sensing. 2020; 12(7):1085. https://doi.org/10.3390/rs12071085
Chicago/Turabian StyleZhang, Weixing, Anna K. Liljedahl, Mikhail Kanevskiy, Howard E. Epstein, Benjamin M. Jones, M. Torre Jorgenson, and Kelcy Kent. 2020. "Transferability of the Deep Learning Mask R-CNN Model for Automated Mapping of Ice-Wedge Polygons in High-Resolution Satellite and UAV Images" Remote Sensing 12, no. 7: 1085. https://doi.org/10.3390/rs12071085
APA StyleZhang, W., Liljedahl, A. K., Kanevskiy, M., Epstein, H. E., Jones, B. M., Jorgenson, M. T., & Kent, K. (2020). Transferability of the Deep Learning Mask R-CNN Model for Automated Mapping of Ice-Wedge Polygons in High-Resolution Satellite and UAV Images. Remote Sensing, 12(7), 1085. https://doi.org/10.3390/rs12071085