To detect objects in remote sensing imagery remains highly challenging due to complex background clutter, extreme variations in object scale, and high sensitivity to arbitrary orientations. To address these issues, this paper proposes DMADet, a novel detection framework based on dual-domain feature extraction and multi-dimensional focused diffusion, aiming to enhance perception capability and robustness in complex remote sensing scenarios. The method first introduces a dual-domain collaborative network (DDCNet) that jointly exploits spatial and frequency domain information: edge-aware features in the spatial domain are strengthened using Scharr operators, while global contextual representations are captured in the frequency domain via Fast Fourier transform, enabling bidirectional interaction and complementary fusion between the two domains. Second, to alleviate semantic degradation across layers in conventional feature pyramids, a multi-dimensional focused diffusion pyramid (MFDPN) is developed, which employs a channel-block weighting strategy to adaptively integrate high- and low-level features and incorporates a lightweight dual-stream collaborative attention module (LDC-Attention) to enhance multi-scale contextual awareness. Finally, an adaptive rotation-aware detection head is designed, leveraging a dynamic routing mechanism to generate orientation-sensitive convolution kernels, thereby significantly improving rotational invariance and detection accuracy for arbitrarily oriented objects. Extensive experiments demonstrate that DMADet achieves mAP@0.5 scores of 75.9%, 96.8%, and 85.7% on the DOTA-v1.0, NWPU VHR-10, and RSOD benchmark datasets, respectively—consistently outperforming current state-of-the-art methods. These results validate the effectiveness and superiority of the proposed approach in mitigating performance degradation caused by background interference, scale diversity, and rotational variance, while substantially improving object localization accuracy and robustness in real-world remote sensing applications.