  Offshore wind farms represent a renewable energy source with a significant
global growth trend, and their monitoring is strategic for territorial and
environmental planning. This study's primary objective is to detect offshore
wind plants at an instance level using semantic segmentation models and
Sentinel-1 time series. The secondary objectives are: (a) to develop a database
consisting of labeled data and S-1 time series; (b) to compare the performance
of five deep semantic segmentation architectures (U-Net, U-Net++, Feature
Pyramid Network - FPN, DeepLabv3+, and LinkNet); (c) develop a novel
augmentation strategy that shuffles the positions of the images within the time
series; (d) investigate different dimensions of time series intervals (1, 5,
10, and 15 images); and (e) evaluate the semantic-to-instance conversion
procedure. LinkNet was the top-performing model, followed by U-Net++ and U-Net,
while FPN and DeepLabv3+ presented the worst results. The evaluation of
semantic segmentation models reveals enhanced Intersection over Union (IoU)
(25%) and F-score metrics (18%) with the augmentation of time series images.
The study showcases the augmentation strategy's capability to mitigate biases
and precisely detect invariant targets. Furthermore, the conversion from
semantic to instance segmentation demonstrates its efficacy in accurately
isolating individual instances within classified regions - simplifying training
data and reducing annotation effort and complexity.
