[1]
Muhammad Ahmad, Ihsan ul Haq, Qaisar Mushtaq, and Muhammad Sohaib, A New Statistical Approach for Band Clustering and Band Selection Using K-Means Clustering, International Journal of Engineering and Technology vol. 3, no. 6, pp.606-614, (2011).
DOI: 10.7763/ijcce.2013.v2.148
Google Scholar
[2]
Information on: http: /www. csr. utexas. edu/projects/rs/hrs/hyper. html.
Google Scholar
[3]
Martinez-Usomartinez-Uso, F. Pla, J. M. Sotoca, and P. Garcia-Sevilla, Clustering-Based Hyperspectral Band Selection Using Information Measures [J], IEEE Transactions on Geosciences and Remote Sensing, vol. 45, pp.158-4171, Dec. (2007).
DOI: 10.1109/tgrs.2007.904951
Google Scholar
[4]
J. Scott, Remote Sensing: The Image chain Approach. Oxford University Press, New York (1997).
Google Scholar
[5]
C. I Chang, Hyperspectral Data Exploitation: Theory and Applications. Wiley Interscience, (2007).
Google Scholar
[6]
C. S. Dimitris Manolakis and G. Shaw, Hyperspectral subpixel target detection using the linear mixing model, IEEE Transaction on Geoscience and Remote Sensing, vol. 39, July (2001).
DOI: 10.1109/36.934072
Google Scholar
[7]
W. Wei and T. Adali, Detection using correlation bound in a linear mixure model, Signal Processing, vol. 87, p.1118–1127, (2007).
Google Scholar
[8]
M. P. Jos´e and M. B. -D. Jos´e, Blind hyperspectral unmixing, in Image and Signal Processing for Remote Sensing XIII (Lorenzo, ed. ), vol. 6748, SIE, (2007).
Google Scholar
[9]
J. B. Adams and M. O. Smith, spectral mixture modeling : a new analysis of rock and soil types at the vicking lander site, Geophysical Research, vol. 91, no. B8, p.8098–8112, (1986).
DOI: 10.1029/jb091ib08p08098
Google Scholar
[10]
Information on: http: /speclab. cr. usgs. gov/cuprite. html.
Google Scholar
[11]
Muhammad Sohaib, Ihsan-ul-Haq and Qaiser Mushtaq Dimensional Reduction of Hyperspectral Image Data Using Band Clustering and Selection", in proceeding of Global Congress on Science and Engineering (GCSE 2011), Dubai, UAE.
DOI: 10.7763/ijcce.2013.v2.148
Google Scholar
