[1] |
Tong Qingxi,Zhang Bing,Zheng Lanfen.Hyperspectral remote sensing:Theory, technology and applications[M]. Beijing:Higher Education Press, 2006.(in Chinese)
|
[2] |
Wright J,Yang Y A,Ganesh A,et al.Robust face recognition via sparse representation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(2):210227.
|
[3] |
Chen Yi,Nasrabadi M N, Tran D T.Hyperspectral image classification using dictionarybased sparse representation[J].IEEE Transactions on Geoscience and Remote Sensing,2011,49(10):39733985.
|
[4] |
Siddiqui S, Robila S, Peng Jing, et al.Sparse representations for hyperspectral data classification[C]∥Proc of IEEE International Geoscience and Remote Sensing Symposium, 2008:II577II580.
|
[5] |
CampsValls G, Bruzzone L. Kernelbased methods for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(6):112.
|
[6] |
Liu Xiaofang. Research on remote sensing image classification methods based on kernel theory[D].Chengdu:University of Electronic Science and Technology of China, 2011.(in Chinese)
|
[7] |
Yin Jun,Liu Zhonghua,Jin Zhong,et al. Kernel sparse representation based classification[J]. Neurocomputing, 2012, 77(1):120128.
|
[8] |
Liu Jianjun, Wu Zebin, Wei Zhihui, et al. Spatial correlation constrained sparse representation for hyperspectral image classification[J]. Journal of Electronics & Information Technology, 2012, 34(11):26662671.(in Chinese)
|
[9] |
Liu Jianjun, Wu Zebin, Wei Zhihui, et al. Spatialspectral kernel sparse representation for hyperspectral image classification[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(6):24622471.
|
[10] |
Farber R. CUDA application design and development[M].Yu Yulong, Tang Kun,translation. Beijing:China Machine Press, 2013.(in Chinese)
|
[11] |
Santos L,Magli E, Vitulli R, et al. Highlyparallel GPU architecture for lossy hyperspectral image compression[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(2):670681.
|
[12] |
Wu Xianyun, Huang B, Plaza A, et al. Realtime implementation of the pixel purity index algorithm for endmember identification on GPUs[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 11(5):15.
|
[13] |
Nascimento P M J, BioucasDias M J,Alves R M J, et al. Parallel hyperspectral unmixing on GPUs[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 11(3):666670.
|
[14] |
Wu Zebin, Ye Shun, Wei Jie, et al. Parallel optimization of hyperspectral unmixing based on sparsity constrained nonnegative matrix factorization[C]∥Proc of IEEE International Geoscience and Remote Sensing Symposium, 2013:14381441.
|
[15] |
He Guojing, Liu Delian, Zhang Jianqi. High speed spectral matching approach for hyperspectral image based on CUDA[J]. Aero Weaponry, 2011(4):312. (in Chinese)
|
[16] |
Luo Yaohua, Guo Ke, Wang Daming, et al. Hyperspectral remote sensing classification processing parallel computing research based on GPU[C]∥Proc of International Conference on Computer Science and Electronics Engineering, 2012:258261.
|
[17] |
Chen Y, Nasrabadi N M, Tran T D. Hyperspectral image classification using dictionary based sparse representation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(10):39733985.
|
[18] |
Chen Y, Nasrabadi N M, Tran T D. Classification for hyperspectral imagery based on sparse representation[C]∥Proc of Hyperspectral Image and Signal Processing:Evolution in Remote Sensing, 2010:14.
|
[19] |
CULA [EB/OL].[20140507]. http:∥www.culatools.com.
|
[20] |
Ham J,Chen Yangchi, Crawford M M, et al. Investigation of the random forest framework for classification of hyperspectral data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(3):492501.
|
[21] |
CampsValls G, Marsheva B V T, Zhou Dengyong. Semisupervised graphbased hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(10):30443054.
|
[22] |
Plazaet A, Benediktsson A J, Boardman W J,et al. Recent advances in techniques for hyperspectral image processing[J]. Remote Sensing of Environment,2009,113(S1):S110S122.
|
[23] |
CUDA toolkit documentation[EB/OL].[20131005].http:∥docs.nvidia.com/cuda/index.html.
|
|
附中文参考文献:
|
[1] |
童庆禧, 张兵, 郑兰芬.高光谱遥感:原理、技术与应用[M]. 北京:高等教育出版社, 2006.
|
[6] |
刘小芳. 基于核理论的遥感图像分类方法研究[D].成都:电子科技大学,2011.
|
[8] |
刘建军,吴泽彬,韦志辉,等. 基于空间相关性约束稀疏表示的高光谱图像分类[J]. 电子与信息学报,2012,34(11):26662671.
|
[10] |
Farber R. 高性能CUDA应用设计[M]. 于玉龙, 唐堃,译. 北京:机械工业出版社, 2013.
|
[15] |
何国经,刘德连,张建奇. CUDA架构下高光谱图像光谱匹配的快速实现[J]. 航空兵器, 2011(4):312.
|