[1] Liu Yin-Nian, Sun De-Xin, Hu Xiao-Ning et al. Development of visible and short-wave infrared hyperspectral imager onboard GF-5 satellite[J]. Journal of Remote Sensing(Chinese), 24, 333-344(2020).

[2] Reed I S, Yu X. Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution[J]. IEEE Transactions on Acoustics Speech & Signal Processing, 38, 1760-1770(1990).

[3] He Lin, Pan Quan, Zhao Yong-Qiang et al. CFAR target detection in unknown background based on subspace projection in aerial hyperspectral imagery[J]. Chinese Journal of Aeronautics, 27, 657-662(2006).

[4] Zhao Chun-Hui, Yao Xi-Feng. Local kernel RX algorithm-based hyperspectral real-time detection[J]. Journal of Infrared Millimeter Waves, 35, 708-714(2016).

[5] Shi Zhen-Wei, Wu Jun, Yang Shuo et al. RX and its variants for anomaly detection in hyperspectral images[J]. Infrared and Laser Engineering, 41, 796-802(2012).

[6] Pu Xiao-Feng, Lei Wu-Hu, Zhang Lin-Hu et al. Anomaly detection based on improved RX algorithm in hyperspectral imagery[J]. Journal of Image & Graphics, 16, 1632-1636(2011).

[7] Zhao Chun-Hui, Li Jie, Mei Feng. A kernel weighted RX algorithm for anomaly detection in hyperspectral imagery[J]. Journal of Infrared Millimeter Waves, 29, 378-382(2010).

[8] He Yuan-Lei, Wang Jing-Li, Jia Jun-Bo et al. Improved ACE target detection algorithm for hyperspectral remote sensing images[J]. Journal of Shandong University of Science and Technology (Natural Science), 34, 62-67(2015).

[9] Du Bo. Sub-pixel target detection from hyperspectral remote sensing[D](2010).

[10] Feng Ru-Yi, Wang Li-Zhe, Zeng Tie-Yong. Review of hyperspectral remote sensing image subpixel information extraction[J]. Acta Geodaetica et Cartographica Sinica, 52, 1187-1201(2023).

[11] Heinz D C, Chein-I-Chang. Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery[J]. IEEE Transactions on Geoscience & Remote Sensing, 39, 529-545(2002).

[12] Ren H, Chang C I. Automatic spectral target recognition in hyperspectral imagery[J]. IEEE Transactions on Aerospace and Electronic Systems, 39, 1232-1249(2003).

[13] Tu T M, Chen C H, Chang C I. A least squares orthogonal subspace projection approach to desired signature extraction and detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 35, 127-139(1997).

[14] Tu T M, Shyu H C, Lee C H et al. An oblique subspace projection approach for mixed pixel classification in hyperspectral images[J]. Pattern Recognition, 32, 1399-1408(1999).

[15] Brumbley C, Chang C I. An unsupervised vector quantization-based target subspace projection approach to mixed pixel detection and classification in unknown background for remotely sensed imagery[J]. Pattern Recognition, 32, 1161-1174(1999).

[16] Scharf L L, Friedlander B. Matched subspace detectors[J]. IEEE Transactions on Signal Processing, 42, 2146-2157(1994).

[17] Yuam Z, Sun H, Ji K et al. Local sparsity divergence for hyperspectral anomaly detection[J]. IEEE Geoscience and Remote Sensing Letters, 11, 1697-1701(2014).

[18] Li J Y, Zhan H Y, Zhan L P et al. Hyperspectral anomaly detection by the use of background joint sparse representation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8, 2523-2533(2015).

[19] Zhan Y X, Du B, Zhan L P et al. Joint sparse representation and multi-task learning for hyperspectral target detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 55, 894-906(2017).

[20] Li W, Du Q. Collaborative representation for hyperspectral anomaly detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 53, 1463-1474(2015).

[21] Ling Qiang, Huang Shu-Cai, Wei Dao-Zhi et al. Collaborative representation based binary hypothesis model for hyperspectral target detection[J]. Acta Electronica Sinica, 44, 1187-1201(2016).

[22] Taghizadeh M, Chalechale A. A comprehensive and systematic review on classical and deep learning based region proposal algorithms[J]. Expert Systems with Application, 116105(2022).

[23] Liu C Y, Zhang Y L, Bi X J. Urban traffic object detection based on multi-stage proposal sparse R-CNN[J]. Acta Electronica Sinica, 51, 26-31(2023).

[24] Zhang J G, Zhang G Y, Yang Q et al. Review of recognition of aircraft contrails and their radiative forcing[J]. Trans. Atmos. Sci., 41, 577-584(2018).

[25] Liu C, Wei Z Q. Numerical simulation on condensation tail characteristics of a civil aviation engine[J]. China Science Paper, 16, 649-656(2021).

[26] Fan Z Y. Aircraft wake cloud recognition based on satellite images[J]. China Flights, 73-76(2022).

[27] Engelstad M, Sengupta S K, Lee T et al. Automated detection of jet contrails using the AVHRR split window[J]. International Journal of Remote Sensing, 13, 1391-1412(1992).

[28] Mannstein H, Meyer R, Wendling P. Operational detection of contrails from NOAA-AVHRR data[J]. International Journal of Remote Sensing, 20, 1641-1660(1999).

[29] Yang L F, Feng Y Q, Wang Y M et al. Refined fire detection and band selection method in hyperspectral remote sensing imagery based on sparse-VIT[J]. Infrared Physics & Technology, 2024 137, 105104.

[30] Gioi R G V, Jakubowicz Jérémie, Morel J M et al. LSD: A line segment detector[J]. Image Processing On Line, 2, 35-55(2012).

[31] Li H, Chen Q, Xu Y X. Method of ensemble empirical mode decomposition with partial adaptive noise[J]. Journal of Nanjing University of Science and Technology, 48, 227-234(2024).

[32] Chen Z G, Shu J. Empirical mode decomposition on removing spectral noise in hyperspectral image[J]. Journal of Infrared and Millimeter Waves, 27, 378-382(2008).

[33] Shen Y, Zhang M. Hyperspectral images classification based on wavelet threshold denoising and empirical mode decomposition[J]. Journal of Astronautics, 33, 471-477(2012).

[34] Demir B, Erturk S. An empirical mode decomposition and composite kernel approach to increase hyperspectral image classification accuracy[C], 855-858(2010).

[35] Erturk A, Gullu M K, Erturk S. Hyperspectral image classification using empirical mode decomposition with spectral gradient enhancement[J]. IEEE Transactions on Geoscience & Remote Sensing, 51, 2787-2798(2013).

[36] Shen Yi, Zhang Min, Zhang Miao. Mutual information bands selection and empirical mode decomposition based support vector machines for hyperspectral data high-accuracy classification[J]. Laser & Optoelectronics Progress, 48, 62-69(2011).

[37] Wang Li-Guo, Wan Yu-Mei, Lu Ting-Ting et al. Hyperspectral image classification by combining empirical mode decomposition with gabor filtering[J]. Journal of Harbin Engineering University, 37, 284-290(2016).

[38] Cao Jing-Jing, Zhuo Li, Wang Fang et al. Research on applications of blind source separation techniques in hyperspectral unmixing[J]. Remote Sensing Technology and Application, 28, 488-495(2013).

[39] Li Le, Zhang Yu-jin. A survey on algorithms of non-negative matrix factorization[J]. Acta Electronica Sinica, 36, 737-743(2008).