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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.