Preprint Article Version 1 This version not peer reviewed

Assessing Uncertainty in LULC Classification Accuracy by Using Bootstrap Resampling

Version 1 : Received: 26 August 2016 / Approved: 26 August 2016 / Online: 26 August 2016 (11:56:26 CEST)

A peer-reviewed article of this Preprint also exists.

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

Journal reference: Remote Sens. 2016, 8, 705
DOI: 10.3390/rs8090705

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.

Subject Areas

land-use/land-cover (LULC); uncertainty; bootstrap resampling; chi-square threshold; class probability vector (CPV); entropy

Readers' Comments and Ratings (0)

Leave a public comment
Send a private comment to the author(s)
Rate this article
Views 0
Downloads 0
Comments 0
Metrics 0
Leave a public comment

×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.