Abstract
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.
Abstract (translated)
我们基于分层度量学习的范式,解决了光学遥感(RS)图像的场景分类问题。理想情况下,受监督的度量学习策略从一组训练数据点学习投影,以便最小化类内方差,同时最大化类标签空间的类间可分性。然而,标准度量学习技术在学习变换矩阵时不包括类交互信息,这通常被认为是处理细粒度视觉类别时的瓶颈。作为补救措施,我们建议通过探索它们的视觉相似性以分层方式组织类,并随后为树的非叶节点处出现的类学习单独的距离度量变换。我们采用迭代最大边际聚类策略来获得类的层次结构。在大规模NWPU-RESISC45和流行的UC-Merced数据集上获得的实验结果证明了所提出的基于RS场景识别策略的分层度量学习与标准方法相比的功效。
URL
https://arxiv.org/abs/1708.01494