Scene Classification via Triplet Networks

Scene classification is a fundamental task for automatic remote sensing image understanding. In recent years, convolutional neural networks have become a hot research topic in the remote sensing community, and have made great achievements in scene classification. Deep convolutional networks are primarily trained in a supervised way, requiring huge volumes of labeled training samples. However, clearly labeled remote sensing data are usually limited. To address this issue, in this paper, we propose a novel scene classification method via triplet networks, which use weakly labeled images as network inputs. Besides, we initiate a theoretical study on the three existing loss functions for triplet networks, analyzing their different underlying mechanisms for dealing with “hard” and/or “easy” triplets during training. Furthermore, four new loss functions are constructed, aiming at laying more stress on “hard” triplets to improve classification accuracy. Extensive experiments have been conducted, and the experimental results show that triplet networks coupled with our proposed losses achieve a state-of-the-art performance in scene classification tasks.