时-谱动态约束的光谱波段自适应成像探测

doi:10.7527/S1000-6893.2025.31963

Abstract

Abstract:

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 narrow-band 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.

Key words: spectral adaptation, information content evaluation, temporal-spectral constraints, imaging detection, hyperspectral imaging

CLC Number:

Yuning WANG, Shaohui MEI, Mingyang MA, Yongzheng REN, Mengtao CAO, Yan FENG. Spectral band adaptive imaging detection with temporal-spectral dynamic constraints[J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(23): 631963.

Figures/Tables 11

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References 29

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高光谱视频	检测器	平均探测精度/%
高光谱视频	检测器	单一波段平均	理论参考标准	光谱自适应模型
车辆	预训练 YOLO v11-s	73.35±2.15	83.41±0.89	78.5±1.23
行人		62.11±3.02	74.93±1.45	71.48±1.67
摩托车		59.76±2.78	65.03±1.12	60.68±1.54
自行车		77.57±1.89	88.32±0.76	80.03±1.05

指标	波段切换最小时间/s
指标	0	0.5	1	1.5
平均探测精度/%	71.48	71.03	69.77	69.19
平均处理时间/s	3.427	2.678	1.399	0.741

方法	平均探测精度/%，平均处理时间/s
	IMEC25数据集				IMEC16数据集
	车辆	行人	摩托车	自行车	鸭子	路人	轿车	电瓶车
ICA	58.64±4.21，0.351	37.45±5.33，0.402	47.83±3.89， 0.386	53.35±3.12，0.391	77.89±4.35，0.281	76.72±5.47，0.321	76.15±4.12，0.309	72.64±3.28，0.31
MVPCA	62.39±2.97，0.285	49.88±3.45，0.33	53.6±2.67，0.329	68.89±2.31，0.313	81.54±3.02，0.228	88.21±3.68，0.264	82.37±2.83，0.263	87.93±2.45，0.25
PDPC	73.81±1.75，0.219	60.32±2.13，0.221	55.96±2.05，0.222	71.19±1.88，0.216	93.25±1.82，0.175	89.67±2.24，0.177	85.43±2.11，0.177	90.85±1.95，0.173
GIE	75.24±1.1，0.471	69.28±1.88，0.503	54.81±0.93，0.501	70.4±1.32，0.484	94.81±2.18，0.377	90.12±1.61，0.402	83.81±0.97，0.401	89.94±1.01，0.39
BABS	76.73±0.01，0.334	68.93±0.13，0.334	55.89±0.45，0.336	75.24±0.88，0.336	95.05±0.15，0.267	90.24±0.08，0.267	87.28±0.09，0.269	90.45±0.42，0.269
光谱自适应	74.82±1.23，0.127	68.03±1.67，0.155	56.02±1.54，0.131	72.68±1.42，0.134	95.06±1.15，0.102	88.41±1.49，0.122	86.78±1.37，0.105	90.92±1.33，0.107

Spectral band adaptive imaging detection with temporal-spectral dynamic constraints

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