Guided Unsupervised Learning by Subaperture Decomposition for Ocean SAR Image Retrieval
This addresses the need for efficient retrieval in large ocean databases for geophysical applications, offering an incremental improvement over existing unsupervised methods.
The paper tackles the problem of automated feature extraction from ocean SAR imagery without requiring labeled data by using subaperture decomposition to enhance unsupervised learning, resulting in over 20% improvement in retrieval precision for a transformer auto-encoder network.
Spaceborne synthetic aperture radar (SAR) can provide accurate images of the ocean surface roughness day-or-night in nearly all weather conditions, being an unique asset for many geophysical applications. Considering the huge amount of data daily acquired by satellites, automated techniques for physical features extraction are needed. Even if supervised deep learning methods attain state-of-the-art results, they require great amount of labeled data, which are difficult and excessively expensive to acquire for ocean SAR imagery. To this end, we use the subaperture decomposition (SD) algorithm to enhance the unsupervised learning retrieval on the ocean surface, empowering ocean researchers to search into large ocean databases. We empirically prove that SD improve the retrieval precision with over 20% for an unsupervised transformer auto-encoder network. Moreover, we show that SD brings important performance boost when Doppler centroid images are used as input data, leading the way to new unsupervised physics guided retrieval algorithms.