Comparative Assessment of Clustering Strategy for Reducing Computational Load in Distributed Fusion Transformer (DAFTA)

Hyperspectral image (HSI) classification is widely used for many critical remote sensing applications that require high accuracy. Their classification is a challenging task due to the large size of HSI datasets. One approach to reduce the size of HSI datasets is to exploit patch similarity. Pixels with the same class tend to exhibit similar features in space, so similar patches are unlikely to provide any new information for classification. (Distributed Architecture for Fusion Transformer training Acceleration) DAFTA is a multi-modal data fusion transformer that integrates information from multiple modalities and sources, such as HSI and LiDAR, to improve classifi-cation accuracy. This work proposes a method for the DAFTA framework to reduce the size of HSI datasets with less clustering time than the existing approach. We use a clustering approach to identify and eliminate similar patches that belong to the same class with the least amount of time. The experimental results show that the proposed method can significantly reduce the size of the training dataset in minimum time without sacrificing classification accuracy, which enhances the efficiency of distributed training.