Reducing Dimensionality of Hyperspectral Data with Robust PCA and Autoencoders in R

<h1>Background</h1> <p>Hyperspectral images are images that capture a large number of spectral bands across the electromagnetic spectrum, usually hundreds of bands. While this provides a wealth of information, it also presents several challenges. One of the primary disadvantages of hyperspectral images is their high dimensionality, which can make them difficult to process and analyze. The large amount of data can also require significant storage capacity and computational resources.</p> <p>Dimensionality reduction techniques can help address these challenges by reducing the size of the data while preserving its important features. This can improve the efficiency of subsequent analysis tasks, such as classification or anomaly detection. Additionally, these techniques can help reduce the impact of noise in the data by removing parts of the information contained within the data which are considered as noise or unimportant information.</p> <p><a href="https://medium.com/@pratama.bima1/reducing-dimensionality-of-hyperspectral-data-with-robust-pca-and-autoencoders-in-r-5d6b6b309655"><strong>Learn More</strong></a></p>