Dynamic Data Augmentation Method for Hyperspectral Image Classification Based on Siamese Structure

Abstract

At present, deep learning classification researches of hyperspectral usually focus on optimizing the classification model. In essence, most of them did not take special measures for the characteristics of the small sample and imbalanced category distribution of hyperspectral itself. Aiming at the problems of small samples and imbalanced category distribution, we propose a dynamic data selection algorithm. For one thing, this algorithm can dynamically select the samples that need data augmentation most. For another, it can be nested in Stochastic gradient descent (SGD) and can be easily implemented. Furthermore, there will be differences between the original and the transformed sample because of data augmentation transformation, which obstructs trained models' performance. Aiming at the difference between the augmented sample and the original sample, we define the similarity score and introduce the Siamese training structure to obtain the similarity score by which we reduce the difference through the SGD algorithm. Experiments show that the method proposed in this article improves the classification results of the backbone training model when using data augmentation for training.