A poi-preserving-based compression method for hyperspectral image

Most lossy compression methods for hyperspectral image (HSI) usually compress the data in this way that focus on preserving low frequency information. However, for some applications such as edge detection, the information which belongs to high frequency is more useful. Thus, a new pixel of interest (POI)-preserving-based HSI compression scheme is proposed. The concept of POI is proposed because some pixels are significant in preserving the main high frequency. Firstly, the POI extraction is performed by unmixing and the mixed pixels are viewed as POI, then the mask of the pixel of interest (MPI) is generated. Secondly, the compression scheme based on the POI preserving is conducted. The spatial and spectral redundancies are reduced, respectively, then a POI-lifting strategy is adopted for preserving the main high frequency information. Finally, bit allocation and encoding to the transformed HSI is performed by SPIHT-TCIRA algorithm, followed by the contextual adaptive arithmetic coder (CAAC). Experiments are implemented using the HSI acquired by the ROSIS Sensor. Results indicate that compared with the common compression method, the POI-preserving-based compression method can keep the key high-frequency information more effectively.