Toward Tightness of Scalable Neighborhood Component Analysis for Remote-Sensing Image Characterization

Deep metric learning methods have recently drawn significant attention in the field of remote sensing (RS), owing to their prominent capabilities for modeling relations among RS images based on their semantic contents. In the context of scene classification and large-scale image retrieval, one of the most prominent deep metric learning methods is the scalable neighborhood component analysis (SNCA), which has demonstrated excellent performance on the locality neighborhood structure in the metric space. However, the standard SNCA has important constraints on separating the hard positive and other negative images in the metric space, and this may become a major limitation when dealing with the large-scale variance problem inherent to RS data. To address this issue, we propose a novel deep metric learning formulation that introduces a new margin parameter to enforce the compactness of the within-class feature embeddings. Based on this innovative scheme, we propose two novel loss functions: 1) T-SNCA-c, where the parameter is based on the cosine similarity, and 2) T-SNCA-a, where the parameter is based on the angular distance. Besides, we exploit memory bank optimization to further enhance the semantic diversity during training. Our experimental results, conducted using three downstream applications ( $K$ -NN classification, clustering, and image retrieval) and two large-scale RS benchmark datasets, demonstrate that the proposed approach can achieve superior performance when compared to current state-of-the-art deep metric learning methods. The codes of this work will be made available online ( https://github.com/jiankang1991/GRSL_TSNCA ).