Generalized Scalable Neighborhood Component Analysis for Single and Multi-Label Remote Sensing Image Characterization

Deep metric learning has recently become a prominent technology for the semantic understanding of remote sensing (RS) scenes due to its great potential for characterizing visual semantics. However, state-of-the-art deep metric learning models are often constrained in RS by the use of single-label annotations, which eventually reduce their capacity to characterize complex aerial scenes. Additionally, many of the existing works are specialized in particular RS applications which constrains the study of their associated metric spaces from a multi-task perspective. In this paper, we propose a new unified deep metric learning approach for both single- and multi-label RS scene characterization while also taking into account different downstream RS applications. Specifically, we extend the Scalable Neighborhood Component Analysis (SNCA) to the multi-label case and propose its generalized version, i.e., GSNCA. Extensive experiments on single- and multi-label RS benchmark datasets have been conducted to evaluate the effectiveness of the proposed method for RS image classification, clustering and retrieval.
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