The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Download
Publications Copernicus
Download
Citation
Articles | Volume XLIII-B3-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 81–86, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-81-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 81–86, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-81-2021

  28 Jun 2021

28 Jun 2021

EXPLORING THE USE OF CLASSIFICATION UNCERTAINTY TO IMPROVE CLASSIFICATION ACCURACY

D. Moraes1,2, P. Benevides1, F. D. Moreira1, H. Costa1,2, and M. Caetano1,2 D. Moraes et al.
  • 1Direção-Geral do Território, Rua da Artilharia Um, 107, 1099-052 Lisboa, Portugal
  • 2NOVA Information Management School (NOVA IMS), Universidade Nova Lisboa, Campus de Campolide, 1070-312 Lisbon, Portugal

Keywords: Supervised Classification, Classification Uncertainty, Remote Sensing, Land Cover, Accuracy Assessment

Abstract. Supervised classification of remotely sensed images has been widely used to map land cover and land use. Since the performance of supervised methods depends on the quality of the training data, it is essential to develop methods to generate an enhanced training dataset. Active learning represents an alternative for such purpose as it proposes to create a dataset of optimized samples, normally collected based on classification uncertainty. However, it is heavily dependent on human interaction, since the user has to label selected samples over a number of iterations. In this paper, we explore the use of uncertainty to improve classification accuracy through a single iteration. We conducted experiments in a region of Portugal (Trás-os-Montes), using multi-temporal Sentinel-2 images. The proposed approach consisted in computing the classification uncertainty of a Random Forest to collect additional training data from areas of high uncertainty and perform a new classification. An accuracy assessment was performed to compare the overall accuracy of the initial and new classifications. The results exhibited an increase in accuracy, though considered not statistically significant. Obstacles related to labelling additional sampling units resulted in a lack of additional training data for various classes, which might have limited the accuracy improvement. Additionally, an uneven proportion of additional training sampling units per class and the collection of new sample data from a limited number of uncertainty regions might also have prevented a higher increase in accuracy. Nevertheless, visual inspection of the maps revealed that the new classification reduced the confusion between some classes.