A simple cleaning procedure for improvement of training site statistics

Author

Gill, Gennette ; Gill, Gennette

Author_Institution

Punjab Remote Sensing Centre, India

fYear

1993

fDate

18-21 Aug 1993

Firstpage

2102

Abstract

For improving the effectiveness of supervised training, a cleaning procedure which operates by selectively dropping training site pixels based on the Mahalanobis distance and class probability has been proposed. The method is iterative and takes into account the spectral overlap in all image hands. The procedure results in greater classification accuracy with a narrower confidence interval. The Bhattacharrya distance measure of class separability improved from an average value of 1.9373 to 1.9797 with a maximum change for a class pair from 1.2671 to 1.9052. The overall classification accuracy increased from 94.74±0.64 to 39.63±0.19

Keywords

geophysical techniques; geophysics computing; image recognition; learning (artificial intelligence); neural nets; remote sensing; Bhattacharrya distance measure; Mahalanobis distance; class probability; class separability; classification accuracy; cleaning procedure; geophysical measurement technique; image classification; image processing; iterative; land surface; neural net; remote sensing; selectively dropping training site pixel; supervised training; training site statistics; Cleaning; Cotton; Data compression; Displays; Dynamic range; Humans; Purification; Remote sensing; Statistics; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium, 1993. IGARSS '93. Better Understanding of Earth Environment., International

Conference_Location

Tokyo

Print_ISBN

0-7803-1240-6

Type

conf

DOI

10.1109/IGARSS.1993.322039

Filename

322039