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, and PCA-DCT at low bitrates.

翻译：高光谱图像记录场景中每个像素的电磁波谱，通常每像素包含数百个通道，其信息量比同样尺寸的典型彩色图像高出一个数量级。因此，随着这些图像获取成本的持续降低，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网络表示的高光谱图像压缩方法：使用具有正弦激活函数的多层感知器网络$\Phi_\theta$来"学习"将像素位置映射到给定高光谱图像$I$的像素强度值。$\Phi_\theta$因此充当该图像的压缩编码。通过在每个像素位置评估$\Phi_\theta$即可重建原始图像。我们在四个基准数据集（Indian Pines、Cuprite、Pavia University和Jasper Ridge）上评估了该方法，结果表明在低比特率条件下，所提方法优于JPEG、JPEG2000和PCA-DCT压缩算法。