HTDFormer: Hyperspectral Target Detection Based on Transformer With Distributed Learning

In recent years, many hyperspectral target detection (HTD) methods based on advanced techniques have been proposed and achieved good results. However, the large amount of data produced by satellites and airborne remote sensing instruments has posed new challenges for efficient target detection of massive hyperspectral images (HSIs). In this paper, we propose a new weakly supervised HTD framework based on a transformer with distributed learning (HTDFormer), which capitalizes on the parallel processing capabilities of multiple workers to efficiently handle large-scale HSIs. Specifically, the HTDFormer framework effectively integrates both spectral and spatial features within a unified optimization procedure via the transformer mechanism. A flexible sample augmentation approach is proposed to overcome the limitations of inadequate well-labeled training instances and meet the requirements of the transformer. To facilitate model training, we introduce the concept of distributed deep learning (DDL) into HTDFormer by leveraging a ring all-reduce (RAR) decentralized architecture, which embeds distributed learning into an HTD framework for the first time. Furthermore, the large-batch training strategy and the gradient compression strategy are employed to enable large-scale distributed processing and reduce communication costs, respectively. Finally, an exponentially constrained nonlinear function is adopted to acquire pixel-level prediction via spectral-spatial fusion. Experimental results demonstrate that the proposed framework achieves promising performance with regard to the increasing scale of real HSIs.