BigEarthNet Deep Learning Models with A New Class-Nomenclature for Remote Sensing Image Understanding

Success of deep neural networks in the framework of remote sensing (RS) image analysis depends on the availability of a high number of annotated images. BigEarthNet is a new large-scale Sentinel-2 benchmark archive that has been recently introduced in RS to advance deep learning (DL) studies. Each image patch in BigEarthNet is annotated with multi-labels provided by the CORINE Land Cover (CLC) map of 2018 based on its most thematic detailed Level-3 class nomenclature. BigEarthNet has enabled data-hungry DL algorithms to reach high performance in the context of multi-label RS image retrieval and classification. However, initial research demonstrates that some CLC classes are challenging to be accurately described by considering only (single-date) Sentinel-2 images. To further increase the effectiveness of BigEarthNet, in this paper we introduce an alternative class-nomenclature to allow DL models for better learning and describing the complex spatial and spectral information content of the Sentinel-2 images. This is achieved by interpreting and arranging the CLC Level-3 nomenclature based on the properties of Sentinel-2 images in a new nomenclature of 19 classes. Then, the new class-nomenclature of BigEarthNet is used within state-of-the-art DL models (namely VGG model at the depth of 16 and 19 layers [VGG16 and VGG19] and ResNet model at the depth of 50, 101 and 152 layers [ResNet50, ResNet101, ResNet152] as well as K-Branch CNN model) in the context of multi-label classification. Experimental results show that the models trained from scratch on BigEarthNet outperform those pre-trained on ImageNet, especially in relation to some complex classes including agriculture and other vegetated and natural environments. All DL models are made publicly available, offering an important resource to guide future progress on content based image retrieval and scene classification problems in RS.