HINER: Neural Representation for Hyperspectral Image

Junqi Shi, Mingyi Jiang, Ming Lu, Tong Chen, Xun Cao, Zhan Ma

Abstract: As a prevalent scientific data format with extensive applications, the efficient compression of hyperspectral images (HSI) and ensuring high-quality downstream tasks have garnered significant attention. This paper introduces HINER, a novel approach for compressing HSI using Neural Representation. 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 inherent capability of HINER to implicitly reconstruct spectral bands using input wavelengths, it can generate arbitrary continuous spectra, even those absent in the original input. Consequently, we propose utilizing Implicit Spectral Interpolation for data augmentation during classification model training, thereby improving overall task accuracy on compressed data. 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.

Primary Subject Area: [Experience] Multimedia Applications

Relevance To Conference: This work contributes to compressing Hyperspectral Images with a novel spectral-wise neural representation, achieving state-of-the-art performance. Additionally, we provide theoretical interpretations of the downstream classification accuracy degradation induced by lossy compression and introduce two simple yet effective methods to mitigate this degradation. These explorations will facilitate more efficient compression of Hyperspectral Images and enable the use of compressed Hyperspectral Images for downstream tasks instead of relying on ground truth data, which benefit applications in agriculture, aerospace industry, remote sensing, etc. Consequently, our work is highly relevant to the conference in the area of Multimedia Applications.

Submission Number: 5263