Onboard Deep Lossless and Near-Lossless Predictive Coding of Hyperspectral Images With Line-Based Attention

Deep learning methods have traditionally been difficult to apply to compression of hyperspectral images onboard spacecrafts due to the large computational complexity needed to achieve adequate representational power, as well as the lack of suitable datasets for training and testing. In this article, we depart from the traditional autoencoder approach, and we design a predictive neural network, called line receptance weighted key value (LineRWKV), which works recursively line by line to limit memory consumption. In order to achieve that, we adopt a novel hybrid attentive-recursive operation that combines the representational advantages of Transformers with the linear complexity and recursive implementation of recurrent neural networks (RNNs). The compression algorithm performs the prediction of each pixel using LineRWKV, followed by entropy coding of the residual. Experiments on multiple datasets show that LineRWKV is highly memory-efficient, significantly outperforms state-of-the-art deep learning methods, and is the first deep learning approach to outperform CCSDS-123.0-B-2 at lossless and near-lossless compression. Promising throughput results are also evaluated on a 7-W embedded system.