MULTIMODAL REMOTE SENSING BENCHMARK DATASETS FOR LAND COVER CLASSIFICATION

Over the past few decades, a large collection of feature extraction and classification algorithms have been developed for land cover mapping using remote sensing data. Although these methods have shown the gradually-increasing performance, their potential inevitably meets the bottleneck due to the lack of high-quality and diversified remote sensing benchmark datasets, particularly for the multimodal cases. Accordingly, this, to a larger extent, limits the development of the corresponding methodologies and the practical application of land cover classification. To this end, we aim in this paper to introduce and build several multimodal remote sensing benchmark datasets for land cover classification. Furthermore, two new multimodal land cover classification benchmark datasets, i.e., Berlin and Augsburg, are openly available. Experiments are conducted on the two datasets for evaluating the performance of several multimodal feature learning and classification methods.