A Triplet Nonlocal Neural Network With Dual-Anchor Triplet Loss for High-Resolution Remote Sensing Image Retrieval

Conventional deep-learning-based retrieval models are generally trained under the framework of scene classification with cross-entropy loss, this way focuses only on the output probability corresponding to the label of input samples, while ignoring the predictive information of other categories, which makes the retrieval accuracy susceptible to the intraclass difference of the image samples. And conventional methods often used fixed-size convolution kernels that only consider the local area with fixed sizes, thus largely ignoring the global information. In response to the above problems, this article constructs a triplet nonlocal neural network (T-NLNN) model that combines deep metric learning and nonlocal operation. The proposed T-NLNN follows the three-branch network design, with shared weights in each branch. We evaluate T-NLNN on three public high-resolution remote sensing datasets, and the experimental results suggest that T-NLNN has discriminative feature learning ability and outperforms other existing algorithms. In addition, we propose a dual-anchor triplet loss function to facilitate the utilization of information in the input samples. The experimental results prove that the proposed dual-anchor triplet loss function works better than the traditional triplet loss function on all datasets.