The large-scale acquisition of multi-temporal hyperspectral images has increased the demand for a more efficient compression strategy to reduce the large size of such images. In this work, we propose a lossless prediction-based compression technique for multi-temporal images. It removes temporal correlations along with spatial and spectral correlation, reducing the size of time-lapse hyperspectral image significantly. It predicts the pixel value of the target image by a linear combination of pixels from already predicted spectral and temporal bands. The weight matrix used in the prediction is updated using the RLS filter. Experimental results demonstrate the optimal number of bands to be selected for prediction, the comparative strength of individual correlations, and effectiveness of the technique in terms of bit-rate. Our results show that including temporal correlations reduces the bit-rate by 24.07% and our model provides optimization of 18.15% in terms of bits per pixel compared to the state-of-the-art method.

中文翻译：

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基于RLS滤波器的多时相高光谱图像压缩

多时相高光谱图像的大规模采集增加了对更有效的压缩策略以减少此类图像的大尺寸的需求。在这项工作中，我们为多时间图像提出了一种基于无损预测的压缩技术。它消除了时间相关性以及空间和光谱相关性，显着减小了延时高光谱图像的大小。它通过来自已经预测的光谱和时间带的像素的线性组合来预测目标图像的像素值。使用 RLS 滤波器更新预测中使用的权重矩阵。实验结果证明了要选择用于预测的最佳频带数、各个相关性的比较强度以及该技术在比特率方面的有效性。