The week's pick
Reseach Article
Dimension Reduction of Multispectral Data using Canonical Analysis
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 70 - Number 21 |
| Year of Publication: 2013 |
| Authors: Rupinder Kaur, Smriti Sehgal |
10.5120/12191-8283
|
Rupinder Kaur, Smriti Sehgal . Dimension Reduction of Multispectral Data using Canonical Analysis. International Journal of Computer Applications. 70, 21 ( May 2013), 18-21. DOI=10.5120/12191-8283
Abstract
Remotely Sensed Images are composite images consisting of large number of spectral bands, from electromagnetic spectrum. Analysis and Implementation of such images is much complex processing and takes lot of time. Therefore, dimension of these images must be reduced before any complex operation is performed. Selecting bands, which have higher capability to discriminate between classes, is a process of reducing number of bands with minimum loss of information [1]. In this paper, Canonical Analysis (CA) is used for band selection based on its discriminating power for classification of various classes. CA is based on Fisher's Linear Discriminant Analysis which maximizes the distance of pixels between classes and simultaneously minimizes the distance between pixels in the same class [5]. It computes eigenvalues and eigenvectors of each band for all the classes. Based on these values, loading factor matrix is computed and the band with highest discriminating power is given highest priority. Band with less priority are not selected leading to reduction of size of the image. Results show that spectral bands 1, 3, 5 are selected using Canonical Analysis whereas bands 4, 3, 2 are selected using Principal Component Analysis from the same LANDSAT image.
References
- Yiting Wang, Shiqi Huang, Daizhi Liu, Baihe Wang Research Institute of High-Tech, Xi'an, China, "Research Advance on Band Selection-based Dimension Reduction of Hyperspectral Remote Sensing Images" IEEE, Transactions on Remote Sensing, Environment and Transportation Engineering, June 2012.
- Biliana S. Paskalva, Majeed M. Hayat, Woo-Yong Jang, and Sanjay Krishna, ECE Department and Center for High Technology Materials, University of New Mexico, USA, "A New Approach for Spatio-Spectral Feature Selection for Sensors with Noisy and Overlapping Spectral Bands", IEEE transactions on Geoscience and Remote Sensing Symposium, July 2008.
- Jos´e Mart´?nez Sotoca, Filiberto Pla, and Jos´e Salvador S´anchez, "Band Selection in Multispectral Images by Minimization of Dependent Information", IEEE Transactions on systems, man, and cybernetics—part c: applications and reviews, vol. 37, no. 2, march 2007.
- Chein-I Chang, Qian Du, Tzu-Lung Sun, and Mark L. G. Althouse, "A Joint Band Prioritization and Band Decorrelation Approach to Band Selection for Hyperspectral Image Classification", IEEE Transactions on Geoscience and Remote Sensing, vol. 37, no. 6, November 1999.
- Te-Ming Tu, Chin-Hsing Chen, Jiunn-Lin Wu, and Chein-I Chang, "A Fast Two-Stage Classification Method for High-Dimensional Remote Sensing Data" IEEE Transactions on Geoscience and Remote Sensing, vol. 36, no. 1, January 1998.
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