Recognition of the Remote Sensing Scenes From Unseen Classes

Existing scene classification models tend to be only capable of recognizing scene images from classes which have been learned by the models. This implies that testing images from classes which are not used for training the models cannot be recognized. There are tens of thousands of scene classes in the real world and it is quite infeasible for us to label all these classes for model training. Therefore, how to develop a classification model that recognizes scene images from unseen classes has been an open problem. To address this issue, we investigate the underlying relations between scene attributes and scene classes. We observe that one scene class can be characterized by a few scene attributes, and furthermore, one unseen scene class can be depicted by seen attributes. In the light of this observation, we train support vector machines to classify scene attributes rather than scene classes. We determine the attributes of a scene image from an unseen class by the support vector machines. This enables us to infer the unseen class of the scene image with respect to the attribute-class relations. Experimental results validate our framework.