Interband Retrieval and Classification Using the Multilabeled Sentinel-2 BigEarthNet Archive

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing 2021, Vol.14, p.9884-9898
• Main Authors: Chaudhuri, Ushasi, Dey, Subhadip, Datcu, Mihai, Banerjee, Biplab, Bhattacharya, Avik
• Format: Article
• Language: English
• Subjects: Agroforestry; Archives & records; Artificial neural networks; Classification; Datasets; Design; Feature extraction; Frameworks; Human-made structures; Interband retrieval; Land cover; Land use; land-cover classification; Machine learning; multilabel classification; multilabel cross triplet loss; multimodal classification; Neural networks; Remote sensing; Resolution; Resolution cell; Retrieval; Satellites; Sensors; Sentinel-2; Soil; Spatial discrimination; Spatial resolution; Task analysis; Vegetation mapping
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2021.3112209

Conventional remote sensing data analysistechniques have a significant bottleneck of operating on a selectively chosen small-scale dataset. Availability of an enormous volume of data demands handling large-scale, diverse data, which have been made possible with neural network-based architectures. This article exploits the contextual information capturing ability of deep neural networks, particularly investigating multispectral band properties from Sentinel-2 image patches. Besides, an increase in the spatial resolution often leads to nonlinear mixing of land-cover types within a target resolution cell. We recognize this fact and group the bands according to their spatial resolutions, and propose a classification and retrieval framework. We design a representation learning framework for classifying the multispectral data by first utilizing all the bands and then using the grouped bands according to their spatial resolutions. We also propose a novel triplet-loss function for multilabeled images and use it to design an interband group retrieval framework. We demonstrate its effectiveness over the conventional triplet-loss function. Finally, we present a comprehensive discussion of the obtained results. We thoroughly analyze the performance of the band groups on various land-cover and land-use areas from agro-forestry regions, water bodies, and human-made structures. Experimental results for the classification and retrieval framework on the benchmarked BigEarthNet dataset exhibit marked improvements over existing studies.