The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4, 201–208, 2018
https://doi.org/10.5194/isprs-archives-XLII-4-201-2018
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4, 201–208, 2018
https://doi.org/10.5194/isprs-archives-XLII-4-201-2018

  19 Sep 2018

19 Sep 2018

IDENTIFICATION OF LOW ACCURACY REGIONS IN LAND COVER MAPS USING UNCERTAINTY MEASURES AND CLASSIFICATION CONFIDENCE

C. C. Fonte1 and L. M. S. Gonçalves2 C. C. Fonte and L. M. S. Gonçalves
  • 1Department of Mathematics, University of Coimbra, P-3001 501 Coimbra, Portugal / Institute for Systems Engineering and Computers at Coimbra, Portugal
  • 2Municipality of Leiria / Institute for Systems Engineering and Computers at Coimbra, Portugal / Polytechnic Institute of Leiria / NOVA IMS, UNL, Portugal

Keywords: Multispectral images, Classification, Uncertainty, Confidence, Accuracy, Spatial variation

Abstract. The aim of this article is to assess if the data provided by soft classifiers and uncertainty measures can be used to identify regions with different levels of accuracy in a classified image. To this aim a soft Bayesian classifier was used, which enables the assignment of classifications confidence levels to all pixels. Two uncertainty measures were also used, namely the Relative Maximum Deviation (RMD) uncertainty measure and the Normalized Entropy (NE). The approach was tested on a case study. A multispectral IKONOS image was classified and the classification uncertainty and confidence where computed and analysed. Regions with different levels of uncertainty and confidence were identified. Reference datasets were then used to assess the classification accuracy of the whole study area and also in the regions with different levels of uncertainty and confidence. A comparative analysis was made on the variation of accuracy and classification uncertainty and confidence along the map and per class. The results show that for the regions with more uncertainty or less confidence the spatially constrained confusion matrices always generate lower values of global accuracy than for global accuracy of the regions with less uncertainty or more confidence. The analysis of the user’s and producer’s accuracy also shows the same general tendency. Proposals are then made on methodologies to use the information provided by the uncertainty and confidence to identify less reliable regions and also to improve classification results using fully automated approaches.