Dimensionality Reduction for Remote Sensing Data Analysis: A Systematic Review of Methods and Applications
It provides a systematic handbook for researchers and practitioners in remote sensing to apply existing methods, but it is incremental as it reviews and organizes existing knowledge without novel contributions.
This paper reviews dimensionality reduction techniques for remote sensing data to address challenges like high dimensionality and sparsity, aiming to enhance tasks such as data compression and prediction, but it does not present new experimental results or concrete numbers.
Earth observation involves collecting, analyzing, and processing an ever-growing mass of data. Automatically harvesting information is crucial for addressing significant societal, economic, and environmental challenges, ranging from environmental monitoring to urban planning and disaster management. However, the high dimensionality of these data poses challenges in terms of sparsity, inefficiency, and the curse of dimensionality, which limits the effectiveness of machine learning models. Dimensionality reduction (DR) techniques, specifically feature extraction, address these challenges by preserving essential data properties while reducing complexity and enhancing tasks such as data compression, cleaning, fusion, visualization, anomaly detection, and prediction. This review provides a handbook for leveraging DR across the RS data value chain and identifies opportunities for under-explored DR algorithms and their application in future research.