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

The automatic and accurate detection of mangroves from remote sensing data is essential to assist in conser-vation 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 di-mensions; (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 x 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) pre-sented the best results for large image classification. The proposed method is suitable and effective for moni-toring mangrove patterns over time, providing accurate maps of these ecosystems.

Automated summary

In this study, a deep learning mangrove dataset was developed using the U-net architecture and different backbones, considering the spatial, temporal, and polarization dimensions in the Southeast region of Brazil. The study also explored the use of radar time series for mangrove detection. The results showed that the combination of VV and VH polarizations, the maximum number of images in the time series, the U-net with the Efficient-net-B7 backbone, and the smallest stride value in the sliding windows approach yielded the best results for mangrove detection.