Synthetic Aperture Radar (SAR) target detection has long been impeded by
inherent speckle noise and the prevalence of diminutive, ambiguous targets.
While deep neural networks have advanced SAR target detection, their intrinsic
low-frequency bias and static post-training weights falter with coherent noise
and preserving subtle details across heterogeneous terrains. Motivated by
traditional SAR image denoising, we propose DenoDet, a network aided by
explicit frequency domain transform to calibrate convolutional biases and pay
more attention to high-frequencies, forming a natural multi-scale subspace
representation to detect targets from the perspective of multi-subspace
denoising. We design TransDeno, a dynamic frequency domain attention module
that performs as a transform domain soft thresholding operation, dynamically
denoising across subspaces by preserving salient target signals and attenuating
noise. To adaptively adjust the granularity of subspace processing, we also
propose a deformable group fully-connected layer (DeGroFC) that dynamically
varies the group conditioned on the input features. Without bells and whistles,
our plug-and-play TransDeno sets state-of-the-art scores on multiple SAR target
detection datasets. The code is available at this https URL

提出了一种基于频域变换的神经网络辅助的合成孔径雷达（SAR）目标检测方法，通过校正卷积偏差并关注高频信息，形成自然的多尺度子空间表示，从多子空间去噪的角度来检测目标，在多个 SAR 目标检测数据集上实现了最先进的成绩。