Hyperspectral images, which record the electromagnetic spectrum for a pixel in the image of a scene, often store hundreds of channels per pixel and contain an order of magnitude more information than a typical similarly-sized color image. Consequently, concomitant with the decreasing cost of capturing these images, there is a need to develop efficient techniques for storing, transmitting, and analyzing hyperspectral images. This paper develops a method for hyperspectral image compression using implicit neural representations where a multilayer perceptron network $\Phi_\theta$ with sinusoidal activation functions ``learns'' to map pixel locations to pixel intensities for a given hyperspectral image $I$. $\Phi_\theta$ thus acts as a compressed encoding of this image. The original image is reconstructed by evaluating $\Phi_\theta$ at each pixel location. We have evaluated our method on four benchmarks -- Indian Pines, Cuprite, Pavia University, and Jasper Ridge -- and we show the proposed method achieves better compression than JPEG, JPEG2000, PCA-DCT, and HVEC at low bitrates.

翻译：高光谱图像记录了场景中每个像素的电磁波谱，通常每个像素存储数百个通道，其信息量比同等大小的典型彩色图像高出一个数量级。因此，随着采集这些图像的成本降低，亟需开发高效的高光谱图像存储、传输与分析技术。本文提出一种基于隐式神经表示的高光谱图像压缩方法，该方法采用包含正弦激活函数的多层感知机网络 $\Phi_\theta$ 来“学习”将给定高光谱图像 $I$ 的像素位置映射至像素强度。$\Phi_\theta$ 因而充当该图像的压缩编码。原始图像可通过在每一个像素位置评估 $\Phi_\theta$ 进行重建。我们在四个基准数据集——印度松林、铜矿、帕维亚大学和贾斯珀岭——上评估了所提方法，结果表明在低比特率条件下，该方法实现了优于JPEG、JPEG2000、PCA-DCT及HVEC的压缩性能。