Hierarchical Metric Learning for Optical Remote Sensing Scene Categorization
This work addresses a bottleneck in remote sensing scene categorization for applications like land use monitoring, though it is incremental as it builds on existing metric learning methods.
The paper tackles the problem of fine-grained scene classification in optical remote sensing images by proposing hierarchical metric learning to incorporate class interaction information, achieving improved recognition results on large-scale datasets like NWPU-RESISC45 and UC-Merced.
We address the problem of scene classification from optical remote sensing (RS) images based on the paradigm of hierarchical metric learning. Ideally, supervised metric learning strategies learn a projection from a set of training data points so as to minimize intra-class variance while maximizing inter-class separability to the class label space. However, standard metric learning techniques do not incorporate the class interaction information in learning the transformation matrix, which is often considered to be a bottleneck while dealing with fine-grained visual categories. As a remedy, we propose to organize the classes in a hierarchical fashion by exploring their visual similarities and subsequently learn separate distance metric transformations for the classes present at the non-leaf nodes of the tree. We employ an iterative max-margin clustering strategy to obtain the hierarchical organization of the classes. Experiment results obtained on the large-scale NWPU-RESISC45 and the popular UC-Merced datasets demonstrate the efficacy of the proposed hierarchical metric learning based RS scene recognition strategy in comparison to the standard approaches.