Segmented principal components transformation for efficient hyperspectral remote-sensing image display and classification
- 1 January 1999
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Geoscience and Remote Sensing
- Vol. 37 (1), 538-542
- https://doi.org/10.1109/36.739109
Abstract
A segmented, and possibly multistage, principal components transformation (PCT) is proposed for efficient hyperspectral remote-sensing image classification and display. The scheme requires, initially, partitioning the complete set of bands into several highly correlated subgroups. After separate transformation of each subgroup, the single-band separabilities are used as a guide to carry out feature selection. The selected features can then be transformed again to achieve a satisfactory data reduction ratio and generate the three most significant components for color display. The scheme reduces the computational load significantly for feature extraction, compared with the conventional PCT. A reduced number of features will also accelerate the maximum likelihood classification process significantly, and the process will not suffer the limitations encountered by trying to use the full set of hyperspectral data when training samples are limited. Encouraging results have been obtained in terms of classification accuracy, speed, and quality of color image display using two airborne visible/infrared imaging spectrometer (AVIRIS) data sets.Keywords
This publication has 6 references indexed in Scilit:
- Efficient maximum likelihood classification for imaging spectrometer data setsIEEE Transactions on Geoscience and Remote Sensing, 1994
- The airborne visible/infrared imaging spectrometer (AVIRIS)Remote Sensing of Environment, 1993
- Remote Sensing Digital Image AnalysisPublished by Springer Nature ,1993
- The K-L Expansion as an Effective Feature Ordering Technique for Limited Training Sample SizeIEEE Transactions on Geoscience and Remote Sensing, 1983
- Linear Statistical Inference and its ApplicationsWiley Series in Probability and Statistics, 1973
- The Divergence and Bhattacharyya Distance Measures in Signal SelectionIEEE Transactions on Communications, 1967