Two-Stage Tripletnet: Light Weight Remote Sensing Scene Classification

Abstract

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Remote sensing scene classification (RSSC) seeks to allocate correct semantic labels to remote sensing images. Recently, numerous algorithms have made significant contributions to enhancing the accuracy of RSSC. However, models with high parameters and computational complexity still dominate. To address this issue, we propose a lightweight network architecture, namely Two-Stage TripletNet. In this proposed algorithm, we employ a two-stage optimizing strategy involving label optimization and loss function optimization. First, a KD-tree generated by remote sensing image features is utilized to produce visual labels. Secondly, we establish the triplet sampling method based on the visual and semantic labels of the images. Finally, the triplet loss and cross-entropy loss are jointly applied to train our model. Experimental results on mainstream datasets demonstrate the effectiveness of our proposed framework. Meanwhile, the two-stage optimizing strategy renders our model more competitive compared to other state-of-the-art algorithms.