DenoDet V2: Phase-Amplitude Cross Denoising for SAR Object Detection
This addresses the problem of noise in SAR object detection for remote sensing applications, representing an incremental advancement over DenoDet V1.
The paper tackles coherent noise in Synthetic Aperture Radar (SAR) object detection by proposing DenoDet V2, which uses a band-wise mutual modulation mechanism to enhance phase and amplitude spectra, achieving a 0.8% improvement on the SARDet-100K dataset and reducing model complexity by half.
One of the primary challenges in Synthetic Aperture Radar (SAR) object detection lies in the pervasive influence of coherent noise. As a common practice, most existing methods, whether handcrafted approaches or deep learning-based methods, employ the analysis or enhancement of object spatial-domain characteristics to achieve implicit denoising. In this paper, we propose DenoDet V2, which explores a completely novel and different perspective to deconstruct and modulate the features in the transform domain via a carefully designed attention architecture. Compared to DenoDet V1, DenoDet V2 is a major advancement that exploits the complementary nature of amplitude and phase information through a band-wise mutual modulation mechanism, which enables a reciprocal enhancement between phase and amplitude spectra. Extensive experiments on various SAR datasets demonstrate the state-of-the-art performance of DenoDet V2. Notably, DenoDet V2 achieves a significant 0.8\% improvement on SARDet-100K dataset compared to DenoDet V1, while reducing the model complexity by half. The code is available at https://github.com/GrokCV/GrokSAR.