 Hyperspectral remote sensing is based on measuring the scattered and reflected electromagnetic signals from the Earth's surface emitted by the Sun. These signals are often degraded by atmospheric effects and instrumental, sensor, noises such as Johnson noise, quantization noise, and shot noise. To reduce these noises, various noise reduction techniques have been proposed in the past decade, including band-wise and three-dimensional low-rank denoising methods. These techniques have been shown to improve the signal-to-noise ratio of the observed data, leading to better performance in hyperspectral image analysis tasks such as classification. Additionally, this paper has discussed the challenges associated with hyperspectral image modeling and denoising, as well as the advantages of using low-ranked denoising techniques compared to other denoising techniques. This article was authored by Bennett Rasty, Paul Schunders, Pedram Gammasai, and others.