Cross A M, Settle J J, Drake N A, et al. Subpixel measurement of tropical forest cover using avhrr data. International Journal of Remote Sensing, 1991,12(5): 1119-1129.
[2]
Quarmby N A, Townshend J R G, et al. Linear mixture modelling applied to AVHRR data for crop area estimation. International Journal of Remote Sensing, 1992, 13 (3): 415-425.
[3]
Boardman J W. Geometric mixture analysis of imaging spectrometry data. IEEE International Geoscience and Remote sensing Symposium, 1994: 2369-2371.
[4]
Foody G M. Approaches for the production and evaluation of fuzzy land cover classifications from remotely -sensed data. International Journal of Remote Sensing, 1996, 17 (7): 1317-1340.
[5]
Masell F, Rodolfi A, Conese C. Fuzzy classification of spatially degraded thematic mapper data for the estimation of sub -pixel components. International Journal of Remote Sensing, 1996, 17(3): 537-551.
Foody G M, Lucas R M, Curran P J et al. Non -linear mixture modelling without end-members using an artificial neural network. International Journal of Remote Sensing, 1997, 18(4): 937-953.
[10]
Carpenter G A, Gopal S, Macomber S et al. A neural network method for mixture estimation for vegetation mapping. Remote Sensing of Environment, 1999, 70 (2): 138- 152.
Li L, Ustin S L, Lay M. Application of multiple endmember spectral mixture analysis (MESMA) to AVIRIS imagery for coastal salt marsh apping: a case study in China Camp, CA, USA. International Journal of Remote Sensing, 2005, 26(23): 5193-5207.
[15]
Roberts D A, Gardner M, Church R, et al. Mapping chaparral in the Santa Monica mountains using multiple endmember spectral mixture models. Remote Sensing of Environment, 1998, 65(3): 267-279.
[16]
García -Haro F J, Sommer S, Kemper T. A new tool for variable multiple endmember spectral mixture analysis (VMESMA). International Journal of Remote Sensing, 2005, 26(10): 2135-2162.
[17]
Franke J, Roberts D A, Halligan K, et al. Hierarchical multiple endmember spectral mixture analysis (MESMA) of hyperspectral imagery for urban environments. Remote Sensing of Environment, 2009, 113(8): 1712-1723.
[18]
Mei T, Mertins A, Yin F, et al. Blind source separation for convolutive mixtures based on the joint diagonalization of power spectral density matrices. Signal Processing, 2008, 88(8): 1990-2007.
[19]
Bijaoui A, Nuzillard D, Barma T D. Bss, classification and pixel demixing. Proceedings of the 5th International Conference on Independent Component Analysis and Blind Source Separation, 2004: 96-103.
Hyvarinen A, Oja E. Independent component analysis: algorithms and applications. Neural Networks, 2000, 13 (4- 5): 411-430.
[22]
Tu T M. Unsupervised signature extraction and separation in hyperspectral images: A noise -adjusted fast independent component analysis approach. Optical Engineering, 2000, 39(4): 897-906.
[23]
Zhang X, Chen C H. New independent component analysis method using higher order statistics with application to remote sensing images. Optical Engineering, 2002, 41 (7): 1717-1728.
Woodcock C E, Strahler A H. The factor of scale in remote sensing. Remote Sensing of Environment, 1987, 21(3): 311-322.
[28]
Roberts D A, Smith M O, Adams J B. Green vegetation, nonphotosynthetic vegetation, and soils in aviris data. Remote Sensing of Environment, 1993, 44(2-3): 255-269.
[29]
Smith M O, Ustin S L, Adams J B, et al. Vegetation in deserts: I. a regional measure of abundance from multispectral images. Remote Sensing of Environment, 1990, 31(1): 1-26.
[30]
Small C. Estimation of urban vegetation abundance by spectral mixture analysis. International Journal of Remote Sensing, 2001, 22(7): 1305-1334.
[31]
Green A A, Berman M, Switzer P et al. A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Transactions on Geoscience and Remote Sensing, 1988, 26(1): 65-74.
[32]
Chang C I, Plaza A. A fast iterative algorithm for implementation of pixel purity index. IEEE Geoscience and Remote Sensing Letters, 2006, 3(1): 63-67.
[33]
Plaza A, Chang C I. Fast implementation of pixel purity index algorithm. SPIE Symposium on Defense and Security, XI Conference on Algorithms and Technologies for Multispectral, Hyperspectral and Ultraspectral Imagery, 2005.
[34]
Bateson A, Curtiss B. A method for manual endmember selection and spectral unmixing. Remote Sensing of Environment, 1996, 55(3): 229-243.
[35]
Lelong C C D, Pinet P C, Poilv H. Hyperspectral imaging and stress mapping in agriculture: A case study on wheat in Beauce(France). Remote Sensing of Environment, 1998, 66(2): 179-191.
[36]
Nascimento J M P, Dias J M B. Vertex component analysis: A fast algorithm to unmix hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(4): 898-910.
[37]
Gruninger J, Ratkowski A J, Hoke M L. The sequential maximum angle convex cone (SMACC) endmember model. Proceedings SPIE, Algorithms for Multispectral and Hyper- spectral and Ultraspectral Imagery, 2004.
[38]
Plaza A, Chang C I. An improved N-FINDR algorithm in implementation. SPIE Symposium on Defense and Security, XI Conference on Algorithms and Technologies for Multispectral, Hyperspectral and Ultraspectral Imagery, 2005.
Bastin L. Comparison of fuzzy c-means classification, linear mixture modelling and MLC probabilities as tools for unmixing coarse pixels. International Journal of Remote Sensing, 1997, 18(17): 3629-3648.
[41]
Holben B N, Shimabukuro Y E. Linear mixing model applied to coarse spatial resolution data from multispectral satellite sensors. International Journal of Remote Sensing, 1993, 14(11): 2231-2240.
[42]
Keshava N, Mustard J F. Spectral unmixing. Signal Processing Magazine, IEEE, 2002, 19(1): 44-57.
[43]
Ray T W, Murray B C. Nonlinear spectral mixing in desert vegetation. Remote Sensing Of Environment, 1996, 55(1): 59-64.
[44]
Wang J, Chang C I. Applications of independent component analysis in endmember extraction and abundance quantification for hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44 (9): 2601-2616.
[45]
Wang J, Chang C. Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(6): 1586-1600.
[46]
Attias H. Independent factor analysis. Neural Computation, 1999, 11(4): 803-851.
[47]
Nascimento J M P, Dias J M B. Does independent component analysis play a role in unmixing hyperspectral data? IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(1): 175-187.
[48]
Bedini L, Herranz D, Salerno E et al. Separation of correlated astrophysical sources using multiple -lag data covariance matrices. EURASIP Journal on Applied Signal Processing, 2005, (15): 2400-2412.
[49]
Caiafa C F, Proto A N. Separation of statistically dependent sources using an l2-distance non-gaussianity measure. Signal Processing, 2006, 86(11): 3404-3420.
[50]
Caiafa C F, Salerno E, Proto A N, et al. Blind spectral unmixing by local maximization of non -gaussianity. Signal Processing, 2008, 88(1): 50-68.
[51]
Kemp Z. Mapping sub -pixel boundaries from remotely sensed images, Atkinson P M. Innovations in GIS. London: Taylor and Francis, 1997: 166-180.
[52]
Atkinson P M. Super-resolution target mapping from soft classified remotely sensed imagery. Proceedings of the 5th International Conference on Geocomputation, 2001.
[53]
Thornton M W, Atkinson P M, Holland D A.. A linearised pixel-swapping method for mapping rural linear land cover features from fine spatial resolution remotely sensed imagery. Computers & Geosciences, 2007, 33 (10): 1261- 1272.
[54]
Makido Y, Shortridge A. Land Cover Mapping at Sub-Pixel Scales: Unraveling the Mixed Pixel. Proceedings of the 8th International Conference on Geocomputation, University of Michigan, Ann Arbor, Michigan, 2005.
[55]
Thornton M W, Atkinson P M, Holland D A. Sub -pixel mapping of rural land cover objects from fine spatial resolution satellite sensor imagery using super-resolution pixel- swapping. International Journal of Remote Sensing, 2006, 27(3): 473-491.
[56]
Tatem A J, Lewis H G, Atkinson P M, et al. Super-resolution target identification from remotely sensed images using a hopfield neural network. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(4): 781-796.
[57]
Tatem A J, Lewis H G, Atkinson P M, et al. Super-resolution land cover pattern prediction using a hopfield neural network. Remote Sensing of Environment, 2002, 79(1): 1- 14.
[58]
Tatem A J, Lewis H G, Atkinson P M, et al. Increasing the spatial resolution of agricultural land cover maps using a hopfield neural network. International Journal of Geographical Information Science, 2003, 17(7): 647-672.
[59]
Aplin P, Atkinson P M, Curran P J. Fine spatial resolution simulated satellite sensor imagery for land cover mapping in the UK. Remote Sensing of Environment, 1999, 68(3): 206-216.
[60]
Aplin P, Atkinson P M. Sub-pixel land cover mapping for per -field classification. International Journal of Remote Sensing, 2001, 22(14): 2853-2858.
[61]
Mertens K C, Verbeke L P C, Ducheyne E I, et al. Using genetic algorithms in sub -pixel mapping. International Journal of Remote Sensing, 2003, 24(21): 4241-4247.
[62]
Mertens K C, Verbeke L P C, Westra T, et al. Sub-pixel mapping and sub -pixel sharpening using neural network predicted wavelet coefficients. Remote Sensing of Environment, 2004, 91(2): 225-236.
