ACTA AERONAUTICAET ASTRONAUTICA SINICA >
Spectral band adaptive imaging detection with temporal-spectral dynamic constraints
Received date: 2025-03-11
Revised date: 2025-03-24
Accepted date: 2025-04-17
Online published: 2025-05-06
Supported by
National Natural Science Foundation of China(62171381)
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.
Yuning WANG , Shaohui MEI , Mingyang MA , Yongzheng REN , Mengtao CAO , Yan FENG . Spectral band adaptive imaging detection with temporal-spectral dynamic constraints[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2025 , 46(23) : 631963 -631963 . DOI: 10.7527/S1000-6893.2025.31963
| [1] | SONG M P, SHANG X D, WANG Y L, et al. Class information-based band selection for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(11): 8394-8416. |
| [2] | RAM B G, ODUOR P, IGATHINATHANE C, et al. A systematic review of hyperspectral imaging in precision agriculture: Analysis of its current state and future prospects[J]. Computers and Electronics in Agriculture, 2024, 222: 109037. |
| [3] | 张艺伟, 郭焱培, 唐荣, 等. 高光谱遥感在植物多样性研究中的应用进展与趋势[J]. 遥感学报, 2023, 27(11): 2467-2483. |
| ZHANG Y W, GUO Y P, TANG R, et al. Progress and trends of application of hyperspectral remote sensing in plant diversity research[J]. National Remote Sensing Bulletin, 2023, 27(11): 2467-2483 (in Chinese). | |
| [4] | WANG Y, MEI S H, MA M Y, et al. HTACPE: A hybrid transformer with adaptive content and position embedding for sample learning efficiency of hyperspectral tracker[J]. IEEE Transactions on Multimedia, 2025, 27: 2384-2398. |
| [5] | 熊振宇, 崔亚奇, 董凯, 等. 基于属性引导的多源遥感舰船目标可解释融合关联网络[J]. 航空学报, 2023, 44(22): 627476. |
| XIONG Z Y, CUI Y Q, DONG K, et al. Interpretable fusion association network for multi-source remote sensing ship target based on attribute guidance[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(22): 627476 (in Chinese). | |
| [6] | SHANG X D, SONG M P, WANG Y L, et al. Target-constrained interference-minimized band selection for hyperspectral target detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(7): 6044-6064. |
| [7] | FARRELL M D, MERSEREAU R M. On the impact of PCA dimension reduction for hyperspectral detection of difficult targets[J]. IEEE Geoscience and Remote Sensing Letters, 2005, 2(2): 192-195. |
| [8] | LI W, PRASAD S, FOWLER J E, et al. Locality-preserving dimensionality reduction and classification for hyperspectral image analysis[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(4): 1185-1198. |
| [9] | FALCO N, BENEDIKTSSON J A, BRUZZONE L. A study on the effectiveness of different independent component analysis algorithms for hyperspectral image classification[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(6): 2183-2199. |
| [10] | MA M Y, MEI S H, LI F, et al. Spectral correlation-based diverse band selection for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5508013. |
| [11] | XU B Y, LI X H, HOU W J, et al. A similarity-based ranking method for hyperspectral band selection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(11): 9585-9599. |
| [12] | WU M, OU X F, LU Y L, et al. Heterogeneous cuckoo search-based unsupervised band selection for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 62: 5500616. |
| [13] | 王宇轩, 孙晓兵, 提汝芳, 等. 考虑局部密度的共享近邻高光谱图像波段选择方法[J]. 光学学报, 2024, 44(24): 256-266. |
| WANG Y X, SUN X B, TI R F, et al. Method of shared nearest neighbors in hyperspectral image band selection considering local density[J]. Acta Optica Sinica, 2024, 44(24): 256-266 (in Chinese). | |
| [14] | ZHANG H Q, GAO H M, SUN H, et al. A spatial-spectrum fully attention network for band selection of hyperspectral images[J]. IEEE Geoscience and Remote Sensing Letters, 2024, 21: 5507205. |
| [15] | MA M Y, LI F, HU Y, et al. Joint spatial and spectral graph-based consistent self-representation for unsupervised hyperspectral band selection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2024, 62: 5520616. |
| [16] | SUN W W, HE K, YANG G, et al. A cross-scene self-representative network for hyperspectral band selection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2024, 62: 5509212. |
| [17] | XU Y B, LU L Y, SARAGADAM V, et al. A compressive hyperspectral video imaging system using a single-pixel detector[J]. Nature Communications, 2024, 15: 1456. |
| [18] | ZHANG X Y, CHEN B, ZOU W Z, et al. Progressive content-aware coded hyperspectral snapshot compressive imaging[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2024, 34(11): 10817-10830. |
| [19] | HARTLEY R V. Transmission of information[J]. The Bell System Technical Journal, 1965, 7(3): 535-563. |
| [20] | ULABY F T, KOUYATE F, BRISCO B, et al. Textural information in SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 1986, 24(2): 235-245. |
| [21] | VITERBI A. Error bounds for convolutional codes and an asymptotically optimum decoding algorithm[J]. IEEE Transactions on Information Theory, 1967, 13(2): 260-269. |
| [22] | CHEN L L, ZHAO Y Q, CHAN J C, et al. Histograms of oriented mosaic gradients for snapshot spectral image description[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2022, 183: 79-93. |
| [23] | KHANAM R, HUSSAIN M. Yolov11: An overview of the key architectural enhancements[DB/OL]. arXiv preprint: 2410.17725, 2024. |
| [24] | FALCO N, JON A B, LORENZO B. Spectral and spatial classification of hyperspectral images based on ICA and reduced morphological attribute profiles[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(11): 6223-6240. |
| [25] | HE C L, ZHANG Y, GONG D W, et al. A multitask bee colony band selection algorithm with variable-size clustering for hyperspectral images[J]. IEEE Transactions on Evolutionary Computation, 2022, 26(6): 1566-1580. |
| [26] | WANG Q, ZHANG F H, LI X L. Optimal clustering framework for hyperspectral band selection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(10): 5910-5922. |
| [27] | SU Y R, MEI S H, ZHANG G, et al. Gaussian information entropy based band reduction for unsupervised hyperspectral video tracking[C]∥IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium. Piscataway: IEEE Press, 2022: 791-794. |
| [28] | ISLAM M A, ZHOU J, ZHANG W C, et al. Background-aware band selection for object tracking in hyperspectral videos[J]. IEEE Geoscience and Remote Sensing Letters, 2023, 20: 5511305. |
| [29] | XIONG F C, ZHOU J, QIAN Y T. Material based object tracking in hyperspectral videos[J]. IEEE Transactions on Image Processing, 2020, 29: 3719-3733. |
/
| 〈 |
|
〉 |