Synthetic aperture radar （SAR） imagery is characterized by a large number of targets with diverse categories and significant scale variations， as well as highly complex background clutter caused by coherent speckle noise. These inherent properties substantially degrade detection accuracy and pose significant challenges to reliable target detection. To address the problem of insufficient detection performance under such conditions， this paper proposes a SAR target detection algorithm that jointly exploits spatial-channel feature fusion and frequency selection. Specifically， a ResNet-50 network pre-trained on large-scale datasets is adopted as the backbone to extract hierarchical and multi-scale feature representations from SAR images. On this basis， a feature pyramid network （FPN） augmented with a joint multi-scale spatia-channel feature enhancement module is constructed to strengthen the representation capability of features at different scales. This design enables the network to more effectively capture discriminative target information while alleviating the adverse impact of scale diversity among targets. By jointly modeling spatial and channel-wise dependencies， the proposed enhancement module improves feature expressiveness and robustness， particularly for small and weak targets embedded in cluttered backgrounds. Furthermore， a frequency selection module is introduced in the feature domain to explicitly exploit the frequency characteristics of SAR imagery. This module selectively suppresses noise components while preserving informative target-related signals， thereby enhancing target features and improving the signal-to-noise ratio. Through adaptive frequency-domain filtering， the proposed method effectively mitigates the influence of speckle noise without sacrificing critical structural information， leading to more reliable feature representations for subsequent detection. Extensive comparative experiments are conducted on two widely used benchmark datasets， MSAR and SARDet-100K， to evaluate the effectiveness of the proposed approach. Experimental results demonstrate that the proposed algorithm consistently outperforms several representative and state-of-the-art SAR image target detection methods， including Faster R-CNN， ConvNeXt， PVT-T， and YOLOF， across both datasets. These results indicate that the proposed framework achieves superior detection performance and exhibits strong generalization capability under complex SAR imaging conditions. Overall， the proposed method provides an effective solution for improving SAR target detection accuracy in scenarios involving complex backgrounds， severe speckle noise， and multi-scale target distributions.