Wetland Classification with Deep ResU-Net Convolutional Neural Network and Multitemporal Sentinel-1 & 2 Imagery and ALOS Elevation Data: A Case Study in Alberta Parkland & Grassland Natural Region, Canada
The study aimed to develop a Deep Learning (DL) model for a large-scale wetland classification in Alberta's Parkland and Grassland Natural Region (PGNR) using a fusion of multi-temporal Sentinel-2 (S2) optical and Sentinel-1 (S1) radar data and topographic data. A key objective of the study was to compare the performance of the ResNet model with two shallow learning techniques (namely Random Forest (RF) and Support Vector Machine (SVM)). A 25-band multi-seasonal (acquired over the summer/fall months of 2017 to 2020) image stack comprised of S1 (dual-polarization vertical-horizontal (VH) bands) and S2 (near-infrared (band 8) and shortwave infrared (band 11)) images and Advanced Land Observing Satellite (ALOS) derived Topographic Wetness Index as input data in the three models. Comparing the three products' accuracy metrics showed that the CNN model significantly outperformed the shallow machine learning models (SVM and RF). The best performing model was the ResU-Net model, with overall accuracy and overall kappa of 74% and 0.62, followed by SVM (69% and 0.55) and RF (0.68 and 0.54), respectively. The relative F1 scores of the mapped wetlands (marsh, open water, and swamp) using the shallow ML models showed deficiencies in their predictive capabilities. The average F1 score of the ResNet model was 0.77 compared to 0.65 (for SVM) and 0.64 (for RF). Compared to the ResNet CNN predictions, it was evident that this DL technique outperformed the shallow ML techniques evaluated in the study.
- 2022-09-19 first online, published, posted
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