Using pure and mixed objects in the training of object-based image classifications

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 University Park campus of the University of Nottingham were submitted to a series of segmentation analyses in which a range of under- to over-segmentation outputs were intentionally produced. Training objects representing four classes (bare soil, impervious surfaces, vegetation, and water) were selected from the segmentation outputs, resulting in training samples of varying size and proportion of mixed objects. A single-layer artificial neural network equivalent to multinomial logistic regression and able to use both pure and mixed training units was adopted as the classifier. A visual inspection of the results shows that using mixed training objects produced land cover maps of higher quality. Furthermore, the overall and class-specific accuracy of the classifications was systematically higher when mixed training was used (e.g. up to 48% in overall accuracy). The advantage of using mixed objects in training was beneficial even when the size of the mixed training samples was equivalent to that of the pure training samples.