Self-Supervised Image Colorization for Semantic Segmentation of Urban Land Cover
The task of semantic segmentation plays a central role in the analysis of remotely sensed imagery. This relevance is reflected in the act of classifying each image pixel belonging to a particular class. This allows the acquisition of semantic knowledge in form of a classification map, which facilitates decision-making processes. Nowadays, the task of semantic segmentation is mainly solved with Supervised pre-training. It needs plenty of labels to learn a mapping function, which produces useful features. As alternative, Self-supervised learning (SSL) techniques entirely explore the data, find supervision signals and solve a challenge called Pretext task for coming upon robust representations. The current work investigates Image Colorization (IC) as Pretext task to learn feature representations, which will be transferred to an U-Net for predicting semantic segmentations of urban scenes. The study examines two benchmark datasets for validation and generation of classification maps. The results show that the learned features through colorization achieve accurate segmentation results. This was possible both using unlabeled ImageNet training data and the actual datasets. These contain up to half a million examples, which represents a modest amount compared to the number of annotated images present in ImageNet.