Landcover class extraction from Prisma hyperspectral data using Fuzzy Machine Learning Techniques

Abstract

Hyperspectral remote sensing enables highly detailed spectral discrimination between landcover types. However, conventional classification methods often struggle with spectral variability, mixed pixels, heterogeneous landscapes or when limited training data is provided. This study presents a fuzzy machine learning framework for extracting landcover classes from Prisma hyperspectral data, using the strength of soft classification to address classification related challenges.