TY  - JOUR
T1  - Ranking Based Classification in Hyperspectral Images
AU - Aruna Suhasini Devi, Y. 
JO  - Journal of Engineering and Applied Sciences
VL  - 13
IS  - 7
SP  - 1606
EP  - 1612
PY  - 2018
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2018.1606.1612
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2018.1606.1612
KW  - FDPC algorithm
KW  -MPCA (Multilinear Principal Component Analysis) technique
KW  -hyperspectral images
KW  -weighing
KW  -selected features
KW  -classification
AB  - In recent years, the ranking based band selection has been very successful in remote sensing image
classification. Hyperspectral imagery often contains hundreds of images, hence, dimensionality reduction
should be applied to overcome the difficulty of Hughes phenomenon. In this study, two approaches for
efficiency of band selection and robustness are applied. Classification is done using ranking based Fast
Density Peek Clustering (FDPC) algorithm. For FDPC algorithm, first the ranking score of each band is
computed by weighing the normalized local density and the intracluster distance rather than equally taking them
into account. Secondly, an exponential-based learning rule is employed to adjust the cutoff threshold for a
different number of selected bands where it is fixed in the FDPC. Finally, the selected features are processed
by MPCA (Multilinear Principal Component Analysis) technique to reduce the data redundancy and increasing
robustness. From the experimental analysis it is observed that the proposed ranking based classification is more
than efficient and robust when compared with existing band selection techniques.
ER  -