An Automated Machine Learning Framework for Adaptive and Optimized Hyperspectral-Based Land Cover and Land-Use Segmentation

Abstract

Hyperspectral imaging holds significant promise in remote sensing applications, particularly for land cover and land-use classification, thanks to its ability to capture rich spectral information. However, leveraging hyperspectral data for accurate segmentation poses critical challenges, including the curse of dimensionality and the scarcity of ground truth data, that hinder the accuracy and efficiency of machine learning approaches. This paper presents a holistic approach for adaptive optimized hyperspectral-based land cover and land-use segmentation using automated machine learning (AutoML). We address the challenges of high-dimensional hyperspectral data through a revamped machine learning pipeline, thus emphasizing feature engineering tailored to hyperspectral classification tasks. We propose a framework that dissects feature engineering into distinct steps, thus allowing for comprehensive model generation and optimization. This framework incorporates AutoML techniques to streamline model selection, hyperparameter tuning, and data versioning, thus ensuring robust and reliable segmentation results. Our empirical investigation demonstrates the efficacy of our approach in automating feature engineering and optimizing model performance, even without extensive ground truth data. By integrating automatic optimization strategies into the segmentation workflow, our approach offers a systematic, efficient, and scalable solution for hyperspectral-based land cover and land-use classification.