A Modeling and Measurement Approach for the Uncertainty of Features Extracted from Remote Sensing Images

Remote Sensing. 2019;11(16):1841 DOI 10.3390/rs11161841

 

Journal Homepage

Journal Title: Remote Sensing

ISSN: 2072-4292 (Print)

Publisher: MDPI AG

LCC Subject Category: Science

Country of publisher: Switzerland

Language of fulltext: English

Full-text formats available: PDF, HTML

 

AUTHORS

Qi Zhang (School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China)
Penglin Zhang (School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China)
Yao Xiao (State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China)

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 11 weeks

 

Abstract | Full Text

The reliability of remote sensing (RS) image classification is crucial for applying RS image classification results. However, it has received minimal attention, especially the uncertainty of features extracted from RS images. The uncertainty of image features constantly accumulates, propagates, and ultimately affects the reliability and accuracy of image classification results. Thus, research on the quantitative modeling and measurement of the feature uncertainty of RS images is very necessary. To make up for the lack of research on quantitative modeling and measurement of uncertainty of image features, this study first investigates and summarizes the appearance characteristics of the feature uncertainty of RS images in geospatial and feature space domains based on the source and formation mechanisms of feature uncertainty. Then, a modeling and measurement approach for the uncertainty of image features is proposed on the basis of these characteristics. In this approach, a new Local Adaptive Multi-Feature Weighting Method based on Information Entropy and the Local Distribution Density of Points is proposed to model and measure the feature uncertainty of an image in the geospatial and feature space domains. In addition, a feature uncertainty index is also constructed to comprehensively describe and quantify the feature uncertainty, which can also be used to refine the classification map to improve its accuracy. Finally, we propose two effectiveness verification schemes in two perspectives, namely, statistical analysis and image classification, to verify the validity of the proposed approach. Experimental results on two real RS images confirm the validity of the proposed approach. Our study on the feature uncertainty of images may contribute to the development of uncertainty control methods or reliable classification schemes for RS images.