Hierarchical Metric Learning for Optical Remote Sensing Scene Categorization
| Autor: | Goel, Akashdeep, Banerjee, Biplab, Pizurica, Aleksandra |
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| Rok vydání: | 2017 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: Undergoing revision in GRSL |
| Databáze: | arXiv |
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