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Remote Sens. 2016, 8(9), 705; doi:10.3390/rs8090705

Assessing Uncertainty in LULC Classification Accuracy by Using Bootstrap Resampling

1
Newegg, Inc., Taipei 10596, Taiwan
2
Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan
3
Master Program in Statistics, National Taiwan University, Taipei 10617, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editors: Yudong Tian, Ken Harrison, Yoshio Inoue, Clement Atzberger and Prasad S. Thenkabail
Received: 3 May 2016 / Revised: 21 August 2016 / Accepted: 24 August 2016 / Published: 26 August 2016
(This article belongs to the Special Issue Uncertainties in Remote Sensing)
View Full-Text   |   Download PDF [7839 KB, uploaded 26 August 2016]   |  

Abstract

Supervised land-use/land-cover (LULC) classifications are typically conducted using class assignment rules derived from a set of multiclass training samples. Consequently, classification accuracy varies with the training data set and is thus associated with uncertainty. In this study, we propose a bootstrap resampling and reclassification approach that can be applied for assessing not only the uncertainty in classification results of the bootstrap-training data sets, but also the classification uncertainty of individual pixels in the study area. Two measures of pixel-specific classification uncertainty, namely the maximum class probability and Shannon entropy, were derived from the class probability vector of individual pixels and used for the identification of unclassified pixels. Unclassified pixels that are identified using the traditional chi-square threshold technique represent outliers of individual LULC classes, but they are not necessarily associated with higher classification uncertainty. By contrast, unclassified pixels identified using the equal-likelihood technique are associated with higher classification uncertainty and they mostly occur on or near the borders of different land-cover. View Full-Text
Keywords: land-use/land-cover (LULC); uncertainty; bootstrap resampling; chi-square threshold; class probability vector (CPV); entropy land-use/land-cover (LULC); uncertainty; bootstrap resampling; chi-square threshold; class probability vector (CPV); entropy
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Hsiao, L.-H.; Cheng, K.-S. Assessing Uncertainty in LULC Classification Accuracy by Using Bootstrap Resampling. Remote Sens. 2016, 8, 705.

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