Hyperspectral Image Compression Using Sampling and Implicit Neural Representations

Hyperspectral images record the electromagnetic spectrum, and each hyperspectral pixel often stores hundreds of channels. Consequently, a hyperspectral image contains an order of magnitude more information than a similar-sized RGB color image. 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 article develops a method for hyperspectral image compression using implicit neural representations (INRs) where a multilayer perceptron (MLP) network with sinusoidal activation functions “learns” to map pixel locations to pixel spectrum for a given hyperspectral image. This representation, thus, acts as a compressed encoding of this image, and the original image is reconstructed by evaluating this network at each pixel location. We introduce a sampling scheme to achieve better compression times while keeping decoding errors low. The proposed method is evaluated on four benchmarks against 16 other schemes for hyperspectral compression, and according to the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) metrics, the method developed in this article achieves state-of-the-art compression rates at low-bit rates. In addition, we show that the proposed sampling technique reduces encoding times.