Details

Title

A Visual mining based framework for classification accuracy estimation

Journal title

Geodesy and Cartography

Yearbook

2013

Volume

vol. 62

Numer

No 2

Publication authors

Keywords

Data Mining ; remote sensing ; Decision tree ; image classification ; Visualization ; WEKA ; PREFUSE

Divisions of PAS

Nauki Techniczne

Abstract

Classification techniques have been widely used in different remote sensing applications and correct classification of mixed pixels is a tedious task. Traditional approaches adopt various statistical parameters, however does not facilitate effective visualisation. Data mining tools are proving very helpful in the classification process. We propose a visual mining based frame work for accuracy assessment of classification techniques using open source tools such as WEKA and PREFUSE. These tools in integration can provide an efficient approach for getting information about improvements in the classification accuracy and helps in refining training data set. We have illustrated framework for investigating the effects of various resampling methods on classification accuracy and found that bilinear (BL) is best suited for preserving radiometric characteristics. We have also investigated the optimal number of folds required for effective analysis of LISS-IV images.

Publisher

Commitee on Geodesy PAS

Date

2013

Type

Artykuły / Articles

Identifier

ISSN 2080-7636

DOI

10.2478/geocart-2013-0008

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