Classification techniques have been widely used in different remote sensing applications and correct classi-fication of mixed pixels is a tedious task. The problem is more complex with the classification of hyperspectral data and requires a thorough analysis. 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 frame work 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 LISS4 images.
| Published in | American Journal of Remote Sensing (Volume 1, Issue 2) |
| DOI | 10.11648/j.ajrs.20130102.15 |
| Page(s) | 47-52 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2013. Published by Science Publishing Group |
Data mining, Remote sensing, Decision tree, Image classification, Visualization, WEKA, PREFUSE
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APA Style
Arun, Pattathal Vijayakumar. (2013). A visual mining based fame work for classification accuracy estimation. American Journal of Remote Sensing, 1(2), 47-52. https://doi.org/10.11648/j.ajrs.20130102.15
ACS Style
Arun; Pattathal Vijayakumar. A visual mining based fame work for classification accuracy estimation. Am. J. Remote Sens. 2013, 1(2), 47-52. doi: 10.11648/j.ajrs.20130102.15
AMA Style
Arun, Pattathal Vijayakumar. A visual mining based fame work for classification accuracy estimation. Am J Remote Sens. 2013;1(2):47-52. doi: 10.11648/j.ajrs.20130102.15
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Download@article{10.11648/j.ajrs.20130102.15,
author = {Arun and Pattathal Vijayakumar},
title = {A visual mining based fame work for classification accuracy estimation},
journal = {American Journal of Remote Sensing},
volume = {1},
number = {2},
pages = {47-52},
doi = {10.11648/j.ajrs.20130102.15},
url = {https://doi.org/10.11648/j.ajrs.20130102.15},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajrs.20130102.15},
abstract = {Classification techniques have been widely used in different remote sensing applications and correct classi-fication of mixed pixels is a tedious task. The problem is more complex with the classification of hyperspectral data and requires a thorough analysis. 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 frame work 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 LISS4 images.},
year = {2013}
}
TY - JOUR T1 - A visual mining based fame work for classification accuracy estimation AU - Arun AU - Pattathal Vijayakumar Y1 - 2013/04/02 PY - 2013 N1 - https://doi.org/10.11648/j.ajrs.20130102.15 DO - 10.11648/j.ajrs.20130102.15 T2 - American Journal of Remote Sensing JF - American Journal of Remote Sensing JO - American Journal of Remote Sensing SP - 47 EP - 52 PB - Science Publishing Group SN - 2328-580X UR - https://doi.org/10.11648/j.ajrs.20130102.15 AB - Classification techniques have been widely used in different remote sensing applications and correct classi-fication of mixed pixels is a tedious task. The problem is more complex with the classification of hyperspectral data and requires a thorough analysis. 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 frame work 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 LISS4 images. VL - 1 IS - 2 ER -
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