Assessment of the introduction of spatial stratification and manual training in automatic supervised image classification

Abstract

The performance of supervised classification depends on the size and quality of the training data. Multiple studies have used reference datasets to extract training data automatically in an efficient way. However, automatic extraction might be inappropriate for some classes. Furthermore, classes can have distinct spectral characteristics across large areas. Thus, dividing the study area into subregions can be beneficial. This study proposes to assess the impact of the introduction of spatial stratification and manually collected training data on classification performance. Two classifications were conducted with the Random Forest classifier and multi-temporal Sentinel-2 data. The classifications’ performance was evaluated by accuracy metrics and visual inspection of the maps. The results indicate that introducing spatial stratification and manual training yielded a higher overall accuracy (66.7%) when compared to the accuracy of a benchmark classification (60.2%) conducted without stratification and with training data collected exclusively by automatic methods. Visual inspection of the maps also revealed some advantages of the novel approach, namely constraining some land cover classes to be present only within specific strata, which avoids commission errors of the class to spread freely across the map. Most of the classification improvements were observed in subregions with specific landscapes and spectral patterns, although these strata represent a small fraction of the study area, which might have contributed to the small increase in accuracy.