  This paper introduces {HINER}, a novel neural representation for compressing
HSI and ensuring high-quality downstream tasks on compressed HSI. HINER fully
exploits inter-spectral correlations by explicitly encoding of spectral
wavelengths and achieves a compact representation of the input HSI sample
through joint optimization with a learnable decoder. By additionally
incorporating the Content Angle Mapper with the L1 loss, we can supervise the
global and local information within each spectral band, thereby enhancing the
overall reconstruction quality. For downstream classification on compressed
HSI, we theoretically demonstrate the task accuracy is not only related to the
classification loss but also to the reconstruction fidelity through a
first-order expansion of the accuracy degradation, and accordingly adapt the
reconstruction by introducing Adaptive Spectral Weighting. Owing to the
monotonic mapping of HINER between wavelengths and spectral bands, we propose
Implicit Spectral Interpolation for data augmentation by adding random
variables to input wavelengths during classification model training.
Experimental results on various HSI datasets demonstrate the superior
compression performance of our HINER compared to the existing learned methods
and also the traditional codecs. Our model is lightweight and computationally
efficient, which maintains high accuracy for downstream classification task
even on decoded HSIs at high compression ratios. Our materials will be released
at https://github.com/Eric-qi/HINER.
