Hyperspectral remote sensing combines imaging and spectroscopic technologies, serving as a multidimensional information acquisition tool. With the expanding applications of hyperspectral remote sensing, the volume of data has rapidly increased, creating an urgent demand for efficient compression techniques. The strong spatial and spectral correlations inherent in hyperspectral images make data compression feasible. In addition, due to the influence of the same types of gases during imaging, non-adjacent bands may exhibit higher correlations than adjacent bands. This phenomenon suggests that adjusting the sequence of spectral bands can improve the correlation between reference bands. However, most existing hyperspectral image compression methods allow each band to serve as the reference band only for adjacent bands. In reality, a single band may exhibit strong correlations with multiple non-adjacent bands, leading to inefficient use of inter-band correlations. To address this limitation, we propose an approach for optimizing reference bands based on inter-band correlations, integrating the CCSDS-123-B-2 lossless compression standard recommended by the Consultative Committee for Space Data Systems (CCSDS). The proposed method aims to improve the efficiency of inter-band correlation use, enhancing overall compression performance.

The core of this algorithm lies in using the correlation coefficients between spectral bands to select optimal reference bands. By pairing the current band with the newly selected reference band for compression, the algorithm enables the reuse of reference bands. This overcomes the limitation of traditional methods where each band serves as a reference only once. In addition, to further optimize the adjustment and use of reference bands, the algorithm introduces two thresholds: the continuity breakdown threshold and the reference band usage threshold. Experiments are conducted to determine the optimal values for these thresholds, ensuring that the adjusted reference bands achieve superior compression performance.

As shown in Table 4, the computational results demonstrate that the proposed method improves compression performance for hyperspectral images, with greater improvements observed for multispectral images. By adjusting the reference bands, the number of bits required for hyperspectral image compression is reduced, leading to higher compression ratios. Specifically, in experiments involving multispectral image data, the proposed reference band adjustment method improves compression performance by 2.1% to 4.6% compared to the original CCSDS method, showing significant gains. For hyperspectral image data, the method also achieves significant improvements, with compression performance increasing by 1.5% to 2.8% over the original CCSDS method.

In this paper, we propose a reference band adjustment method based on correlation coefficients, which is combined with the CCSDS-123-B-2 standard to present a novel hyperspectral image compression scheme. The core of this method lies in adjusting the reference bands used during prediction based on the correlation coefficients between bands. It also introduces a continuity breakdown threshold and a reference band usage threshold to restrict the adjustment of reference bands. Through a systematic study of the values of these thresholds, this approach addresses the issue of non-reusability of bands in existing band reordering techniques, thus enhancing the efficiency of reference band correlation use. The proposed method has been validated on a range of hyperspectral and multispectral datasets. Experimental results demonstrate that the reference band adjustment method significantly improves the compression performance of hyperspectral images, with a greater enhancement observed in multispectral image data. Specifically, for the multispectral data used in this study, compression performance increases by 2.1% to 4.6%, while for the hyperspectral data, the improvement ranges from 1.5% to 2.8%. Future work will extend the reference band adjustment method from a single band to multiple bands, further improving the utilization of inter-band correlation.