Abstract
This paper presents a new large-scale multi-label Sentinel-2 benchmark archive, named BigEarthNet. Our archive consists of 590,326 Sentinel-2 image patches, each of which has 10, 20 and 60 meter image bands associated to the pixel sizes of 120x120, 60x60 and 20x20, respectively. Unlike most of the existing archives, each image patch is annotated by multiple land-cover classes (i.e., multi-labels) that are provided from the CORINE Land Cover database of the year 2018 (CLC 2018). The BigEarthNet is 20 times larger than the existing archives in remote sensing (RS) and thus is much more convenient to be used as a training source in the context of deep learning. This paper first addresses the limitations of the existing archives and then describes properties of our archive. Experimental results obtained in the framework of RS image scene classification problems show that a shallow Convolutional Neural Network (CNN) architecture trained on the BigEarthNet provides very high accuracy compared to a state-of-the-art CNN model pre-trained on the ImageNet (which is a very popular large-scale benchmark archive in computer vision). The BigEarthNet opens up promising directions to advance operational RS applications and research in massive Sentinel-2 image archives.
Abstract (translated)
URL
https://arxiv.org/abs/1902.06148
