With the increasing number of remote sensing satellites deployed in orbit in China, the quantity of aerospace remote sensing images, represented by Synthetic Aperture Radar (SAR) and optical (RGB) images, is rapidly growing, along with the demand for tasks such as object detection from these massive datasets. However, due to objective factors such as differences in imaging mechanisms and resolutions, images from different satellites exhibit significant modality feature differences. These differences are particularly pronounced between SAR and RGB remote sensing images, making it difficult for a single model to learn feature information across different types of remote sensing images. As a result, each satellite typically requires a dedicated model for detection tasks, which has become a major obstacle to collaborative recognition and relay detection applications in satellite remote sensing. To address this issue, this paper innovatively proposes a self-distillation multimodal detection model based on a Mixture of Experts (MoE). First, a modality-aware MoE structure is constructed, employing a small number of high-quality experts as teachers to guide other experts, while simultaneously incorporating modality-invariant constraints to further reduce cross-modality feature shifts. Second, a Fourier-enhanced diffusion detection head is developed, combining frequency-domain feature enhancement to improve the capability of capturing detailed information of detection targets. To evaluate the model performance, aerospace images were selected and cropped from the public datasets FAIR1M and SARDet_100K, resulting in a dataset of 68 983 aerospace remote sensing images for object detection under different backgrounds and imaging mechanisms. Experimental results demonstrate that, compared with existing single-modality detection methods, the proposed model performs better in detection tasks across both modalities, with a significant improvement in mean Average Precision (mAP). This fully demonstrates that the proposed model possesses significant application value in multimodal aerospace remote sensing image object detection, and exhibits good adaptability to various types of satellite remote sensing images.

Spectral detection technology holds significant application value in the field of UAV perception, where its multi-dimensional information acquisition capability substantially enhances target recognition accuracy and environmental adaptability. However, conventional hyperspectral imaging systems, relying on fixed-band acquisition modes, suffer from complex hardware architectures, low efficiency, and high data redundancy, thereby failing to meet the demand for rapid and low-cost target detection in UAV platforms. To address these challenges, this paper proposes an adaptive spectral band imaging detection algorithm, enabling dynamic adjustment of spectral parameters and the design of structurally simplified optical imaging systems. First, a spectral imaging quality evaluation framework based on information entropy and target separability is established to quantitatively assess the recognition contributions of narrowband spectral data. Second, a spectral adaptive model incorporating constraints of minimum switching time intervals and maximum spectral switching ranges is developed, achieving an optimal balance between target feature discriminability and data dimensionality reduction. Finally, an integrated imaging detection system structure is designed using array-based optical modules. Experimental results validate the effectiveness of the proposed method, offering a novel and efficient solution for UAV-based spectral perception.