https://scholars.lib.ntu.edu.tw/handle/123456789/449007
標題: | Assessing uncertainty in LULC classification accuracy by using bootstrap resampling | 作者: | Hsiao L.-H. Cheng K.-S. KE-SHENG CHENG |
關鍵字: | Bootstrap resampling; Chi-square threshold; Class probability vector (CPV); Entropy; Land-use/land-cover (LULC); Uncertainty | 公開日期: | 2016 | 卷: | 8 | 期: | 9 | 來源出版物: | Remote Sensing | 摘要: | 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. © 2016 by the authors. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/449007 | ISSN: | 2072-4292 | DOI: | 10.3390/rs8090705 | SDG/關鍵字: | Entropy; Land use; Pixels; Probability; Uncertainty analysis; Bootstrap resampling; Chi-square threshold; Class probabilities; Classification accuracy; Classification results; Likelihood technique; Threshold techniques; Uncertainty; Classification (of information) |
顯示於: | 生物環境系統工程學系 |
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