Wetland classification based on depth-adaptive convolutional neural networks using leaf-off SAR imagery

The recent development of deep learning (DL) techniques has created opportunities for classifying wetlands from remote sensing data (mainly optical data). However, the methods for accurately and efficiently classifying large-scale wetlands using DL and radar data that can be more effective than optical data still needs evaluation. In this study, we developed an end-to-end depth-adaptive convolutional neural network (CNN) for mapping wetlands using leaf-off time-series Sentinel-1 Synthetic Aperture Radar (SAR) imagery along with ancillary data. We examined the inclusion of multi-land cover proximity information and a CNN-based self-supervised SAR denoising procedure for enhancing wetland classification accuracy. The depth-adaptive CNN based on U-Net architecture was designed to classify wetland classes (emergent wetland, scrub-shrub wetland, forested wetland, and open water) in Delaware, U.S. while achieving optimization between model complexity (network depths) and accuracy. Results show that our proposed DL method (OA = 0.93, MIoU = 0.60) not only produced a higher classification accuracy than the traditional RF method (OA = 0.89, MIoU = 0.18) but also had a significantly reduced computational cost compared to established state-of-the-art CNNs (e.g., DeepLabV3+ and DANet) without loss of accuracy. The inclusion of multi-land cover proximity information (especially distances to forest and water) and the CNN-based self-supervised SAR denoising procedure can both enhance wetland classification accuracy, especially for forested wetland using traditional RF methods. These results demonstrated the novelty and efficiency of our proposed DL method for classifying wetlands by combing denoised SAR imagery and ancillary information, which provides insights on integration of DL approach and radar data for supporting operational wetland mapping at large spatial scales.