KNN and Convolutional Neural Network for Oil Rig Recognition in Sentinel-1 SAR Images
DOI:
https://doi.org/10.55972/spectrum.v24i1.395Keywords:
Synthetic Aperture Radar (SAR), Automatic Target Recognition (ATR), Machine LearningAbstract
The automatic recognition of targets (oil platforms) using medium-resolution SAR images aids in the surveillance of vast areas such as the South Atlantic. Therefore, this work delved into the study of employing VGG-16 as a feature extractor to feed Machine Learning algorithms, specifically, the kNN. The number of neighbors was varied for a set of SAR image samples from Sentinel-1 containing maritime platforms and false alarms, using an experiment with 50 training and testing blocks. It was demonstrated that the adjustment of classifier parameters results in significant improvements, with an increase of 6.46% in the AUC indicator.
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