BBAuthors: Costa, H.; Advisor: -; Participants: - (2016)
Training of object-based land cover classifications is often performed with objects generated via image segmentation. The objects are commonly assumed to be thematically pure or excluded from training if a mixture of classes is associated with them. However, excluding mixed objects has several consequences such as reducing the size of the training data sets. In this study, it is hypothesized that mixed objects may be used in the training stage of a classification to increase the accuracy with which land cover may be mapped from remotely sensed data, with outputs evaluated in relation to a conventional analysis using only pure objects in training. WorldView-2 data covering the Universi... 
