Journal
REMOTE SENSING
Volume 13, Issue 17, Pages -Publisher
MDPI
DOI: 10.3390/rs13173393
Keywords
machine learning; deep learning; lidar; hyperspectral; remote sensing; urban environment; data fusion; sensor fusion; urban mapping; land cover classification
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Funding
- RFF Oslo og Akershus Regionale forskningsfond
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The combination of hyperspectral and lidar systems has shown promising results in mapping urban environments, with machine learning algorithms playing a crucial role in urban land cover classification. However, challenges remain in extracting key features and managing computational expenses.
Rapid technological advances in airborne hyperspectral and lidar systems paved the way for using machine learning algorithms to map urban environments. Both hyperspectral and lidar systems can discriminate among many significant urban structures and materials properties, which are not recognizable by applying conventional RGB cameras. In most recent years, the fusion of hyperspectral and lidar sensors has overcome challenges related to the limits of active and passive remote sensing systems, providing promising results in urban land cover classification. This paper presents principles and key features for airborne hyperspectral imaging, lidar, and the fusion of those, as well as applications of these for urban land cover classification. In addition, machine learning and deep learning classification algorithms suitable for classifying individual urban classes such as buildings, vegetation, and roads have been reviewed, focusing on extracted features critical for classification of urban surfaces, transferability, dimensionality, and computational expense.
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