Spectral band adaptive imaging detection with temporal-spectral dynamic constraints

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.