[1] ARELLANO P, TANSEY K, BALZTER H, BOYD D S et al. Detecting the effects of hydrocarbon pollution in the Amazon forest using hyperspectral satellite images. Environmental Pollution, 205, 225-238(2015).

[2] BENEDIKTSSON J A, PALMASON J A, SVEINSSON J R. Classification of hyperspectral data from urban areas based on extended morphological profiles. IEEE Transactions on Geoscience and Remote Sensing, 43, 480-491(2005).

[3] HE Youzhu, ZHANG Zhenqian, GUAN Chunyun. Application and prospect of hyperspectral remote sensing technology for precision agricultural monitoring. Crop Research, 29, 96-100(2015).

[4] CHEN Jianming, KUANG Wei, SUN Weidong. Research on the application of airborne hyperspectral remote sensing technology in geological exploration of polymetallic ores. Metallurgical Management, 103-104(2021).

[5] ZHANG Chaoyang, CHENG Haifeng, CHEN Zhaohui et al. Development of hyperspectral remote sensing and its threat to military equipment. Optoelectronic Technology Application, 10-12(2008).

[6] SAMANIEGO L, BARDOSSY A, SCHULZ K. Supervised classification of remotely sensed imagery using a modified KNN technique. IEEE Transactions on Geoscience and Remote Sensing, 46, 2112-2125(2008).

[7] REN Y, ZHANG Y, WEI W et al. A spectral-spatial hyperspectral data classification approach using random forest with label constraints, 344-347(2014).

[8] MELGANI F, BRUZZONE L. Classification of hyperspectral remote sensing images with support vector machines. IEEE Transactions on Geoscience and Remote Sensing, 42, 1778-1790(2004).

[9] LI S, SONG W, FANG L et al. Deep learning for hyperspectral image classification： An overview. IEEE Transactions on Geoscience and Remote Sensing, 57, 6690-6709(2019).

[10] LI Z, LIU M, CHEN Y et al. Deep cross-domain few-shot learning for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 60, 5558-5573(2022).

[11] LI Jingjing, MENG Lichao, ZHANG Ke et al. A review of domain adaptation research. Computer Engineering, 47, 1-13(2021).

[12] ZHANG Y, LI W, ZHANG M et al. Topological structure and semantic information transfer network for cross-scene hyperspectral image classification. IEEE Transactions on Neural Networks and Learning Systems, 32, 5334-5348(2021).

[13] SUN B, FENG J, SAENKO K. Return of frustratingly easy domain adaptation, 2058-2065(2016).

[14] LONG M, WANG J, DING G et al. Transfer feature learning with joint distribution adaptation, 2200-2207(2013).

[15] PAN S J, TSANG I W, KWOK J T et al. Domain adaptation via transfer component analysis. IEEE Transactions on Neural Networks, 22, 199-210(2011).

[16] BLITZER J, MCDONALD R T, PEREIRA F. Domain adaptation with structural correspondence learning, 22-23(2007).

[17] PERSELLO C, BRUZZONE L. Kernel-based domain-invariant feature selection in hyperspectral images for transfer learning. IEEE Transactions on Geoscience and Remote Sensing, 54, 2615-2626(2016).

[18] YE M, QIAN Y, ZHOU J et al. Dictionary learning-based feature-level domain adaptation for cross-scene hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 55, 1544-1562(2017).

[19] FERNANDO B, HABRARD A, SEBBAN M et al. Unsupervised visual domain adaptation using subspace alignment, 2960-2967(2013).

[20] GONG B, SHI Y, SHA F et al. Geodesic flow kernel for unsupervised domain adaptation, 2066-2073(2012).

[21] ZHANG J, LI W, OGUNBONA P. Joint geometrical and statistical alignment for visual domain adaptation, 5150-5158(2017).

[22] BEJIGA M B, MELGANI F, BERALDINI P. Domain adversarial neural networks for large-scale land cover classification. Remote Sensing, 11, 1153(2019).

[23] ZHU Y, ZHUANG F, WANG J et al. Multi-representation adaptation network for cross-domain image classification. Neural Networks, 119, 214-221(2019).

[24] ZHU Y, ZHUANG F, ZHANG X et al. Deep subdomain adaptation network for image classification. IEEE Transactions on Neural Networks and Learning Systems, 32, 1713-1722(2021).

[25] PAUCA V P, PIPER J, PLEMMONS R J. Nonnegative matrix factorization for spectral data analysis. Linear Algebra and Its Applications, 416, 29-47(2006).

[26] JIA S, QIAN Y. Constrained nonnegative matrix factorization for hyperspectral unmixing. IEEE Transactions on Geoscience and Remote Sensing, 47, 161-173(2009).

[27] QIAN Y, JIA S, ZHOU J et al. Hyperspectral unmixing via L1/2 sparsity-constrained nonnegative matrix factorization. IEEE Transactions on Geoscience and Remote Sensing, 49, 4282-4297(2011).

[28] BADEA L. Extracting gene expression profiles common to colon and pancreatic adenocarcinoma using simultaneous nonnegative matrix factorization, 267-278(2008).

[29] ARGYRIOU A, EVGENIOU T, PONTIL M. Multi-task feature learning, 41-48(2006).

[30] GANIN Y, USTINOVA E, AJAKAN H et al. Domain-adversarial training of neural networks. Journal of Machine Learning Research, 17, 1-35(2016).

[31] CHEN L, GAN Z, CHENG Y et al. Graph optimal transport for cross-domain alignment, 1542-1553(2020).

[32] LU Yao, WANG Liguo, SHI Yao. Hyperspectral image classification based on null-spectral feature enhancement under few-shot sample. Journal of Harbin Engineering University, 43, 436-442(2022).

[33] KARANTZALOS K, KARAKIZI C, KANDYLAKIS Z et al. HyRANK： A hyperspectral dataset for benchmarking classification and spectral unmixing. Zenodo(2018).