Remote Sensing, Vol. 18, Pages 417: Small Ship Detection Based on a Learning Model That Incorporates Spatial Attention Mechanism as a Loss Function in SU-ESRGAN

Remote Sensing doi: 10.3390/rs18030417

Authors:
Kohei Arai
Yu Morita
Hiroshi Okumura

Ship monitoring using Synthetic Aperture Radar (SAR) data faces significant challenges in detecting small vessels due to low spatial resolution and speckle noise. While ESRGAN (Enhanced Super-Resolution Generative Adversarial Network) has shown promise for image super-resolution, it struggles with SAR imagery characteristics. This study proposes SA/SU-ESRGAN, which extends the SU-ESRGAN framework by incorporating a spatial attention mechanism loss function. SU-ESRGAN introduced semantic structural loss to accurately preserve ship shapes and contours; our enhancement adds spatial attention to focus reconstruction efforts on ship regions while suppressing background noise. Experimental results demonstrate that SA/SU-ESRGAN successfully detects small vessels that remain undetectable by SU-ESRGAN, achieving improved detection capabilities with a PSNR of approximately 26 dB (SSIM is around 0.5) and enhanced visual clarity in ship boundaries. The spatial attention mechanism effectively reduces noise influence, producing clearer super-resolution results suitable for maritime surveillance applications. Based on the HRSID dataset, a representative dataset for evaluating ship detection performance using SAR data, we evaluated ship detection performance using images in which the spatial resolution of the SAR data was artificially degraded using a smoothing filter. We found that with a 4 × 4 filter, all eight ships were detected without any problems, but with an 8 × 8 filter, only three of the eight ships were detected. When super-resolution was applied to this, six ships were detected.

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Kohei Arai www.mdpi.com