Deep Learning for Multilabel Land Cover Scene Categorization Using Data Augmentation

Abstract

Land cover classification is a flourishing research topic in the field of remote sensing. Conventional methodologies mainly focus either on the simplified single-label case or on the pixel-based approaches that cannot efficiently handle high-resolution images. On the other hand, the problem of multilabel land cover scene categorization remains, to this day, fairly unexplored. While deep learning and convolutional neural networks have demonstrated an astounding capacity at handling challenging machine learning tasks, such as image classification, they exhibit an underwhelming performance when trained with a limited amount of annotated examples. To overcome this issue, this paper proposes a data augmentation technique that can drastically increase the size of a smaller data set to copious amounts. Our experiments on a multilabel variation of the UC Merced Land Use data set demonstrate the potential of the proposed methodology, which outperforms the current state of the art by more than 6% in terms of the F-score metric.