GPU PARALLEL IMPLEMENTATION OF GAS PLUME DETECTION IN HYPERSPECTRAL VIDEO SEQUENCES

Gas plume detection is a challenging task in the field of remote sensing. Due to sensor development, it is now possible to detect and track chemical gas plumes with hyperspectral video sequences (HVS). For the purpose of high detection accuracy, it is important to explore spectral characteristic, and take full advantages of spatial continuity and temporal consistency in HVS. However, the high computational complexity and the large amount of data limit its application in time-critical scenarios. In this paper, we propose a GPU parallel implementation of gas plume detection in HVS, which properly exploits shared memory and intrinsic concurrency of the CUDA blocks, as well as parallel workload assignment and resource allocation. The experimental results demonstrate the proposed GPU parallel method has a considerable acceleration factor while retaining the same detection accuracy compared with the serial and multicore version.