Deep semantic segmentation of mangroves in Brazil combining spatial, temporal, and polarization data from Sentinel-1 time series

Abstract

The automatic and accurate detection of mangroves from remote sensing data is essential to assist in conservation strategies and decision-making that minimize possible environmental damage, especially for the Brazilian coast with continental dimensions. In this context, deep learning segmentation techniques are powerful tools with successful applications in several fields. However, few studies used deep learning for mangrove areas, and none considered radar time series. The present research has the following objectives: (a) develop a deep learning mangrove dataset in the Southeast region of Brazil considering the spatial, temporal, and polarization dimensions; (b) evaluate the U-net architecture with three backbones (ResNet-101, VGG16, and Efficient-net-B7); (c) compare the VV and VH polarizations and their combination; (d) evaluate the time series composition with a varying number of images (29, 15, 8, 4 images); and compare the sliding windows approach using five stride values (8, 16, 32, 64, and 128) for large image classification. This research used the annual Sentinel-1 time series for the period 2017–2020. Image annotation used manual interpretation, and the developed dataset has 2886 images with spatial dimensions of 128 × 128 pixels (2136 for training, 450 for validation, and 300 for testing). We found that the best results considered: (a) both polarizations (VV&VH), (b) the maximum number of images in the time series images (29), (c) the U-net with the Efficient-net-B7 backbone (97.35% overall accuracy, 85.77% precision, 84.96% recall, 85.36% F-score, and 74.46% IoU), and (d) the smallest stride value (8) presented the best results for large image classification. The proposed method is suitable and effective for monitoring mangrove patterns over time, providing accurate maps of these ecosystems.