Abstract
Since multitemporal hyperspectral imaging has an excellent ability to observe the Earth’s surface over time, it has been used for various remote sensing applications. On the other hand, multitemporal hyperspectral images (HSIs) contain HSI sequences acquired multiple times over the same scene, resulting in large amounts of data. Conventional HSI compression methods cannot benefit from temporal correlation, which can be very high, depending on the acquisition cycle. We propose a prediction and compression framework that directly considers temporal correlation for the compression of HSIs. The main objective of the proposed method is to predict each spectral signature in the target HSI from the corresponding spectral signature of the reference HSI using a long short-term memory network model that supports clustering. Then, the residual image between the predicted HSI and the target HSI is quantized and entropy encoded for the compression purpose. The experiments are conducted on a ground-based multitemporal dataset named Noguiero, which contains nine HSIs, in terms of prediction and compression performances. Experiments show that the proposed method not only provides the best quality metrics from the perspective of prediction but also has convincing compression performances compared to the other methods.