Remote sensing framework for geological mapping via stacked autoencoders and clustering

arXiv:2404.02180v1 Announce Type: new
Abstract: Supervised learning methods for geological mapping via remote sensing face limitations due to the scarcity of accurately labelled training data. In contrast, unsupervised learning methods, such as dimensionality reduction and clustering have the ability to uncover patterns and structures in remote sensing data without relying on predefined labels. Dimensionality reduction methods have the potential to play a crucial role in improving the accuracy of geological maps. Although conventional dimensionality reduction methods may struggle with nonlinear …

abstract arxiv autoencoders clustering contrast cs.ai cs.lg data dimensionality face framework limitations mapping patterns sensing supervised learning training training data type unsupervised unsupervised learning via