Best Practices for Urban Hyperspectral Remote Sensing Data Acquisition and Processing

Lulla, Vijay; Jensen, Ryan R.

doi:10.1007/978-3-319-26218-5_4

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…Therefore, the strategy and flight scheme must be adapted to the level of the classification target in an advanced way. The flight line's swath width is reduced at a lower altitude, which requires more flight lines to be flown to cover the target area with changing light conditions due to long integration time [104] and leads to higher off-nadir distortions [105]. However, there are challenges for flying at higher altitudes, such as a high degree of mixed pixels due to a low spatial resolution [106].…”

Section: Hyperspectral (Hs) Imagesmentioning

confidence: 99%

Hyperspectral and Lidar Data Applied to the Urban Land Cover Machine Learning and Neural-Network-Based Classification: A Review

et al. 2021

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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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Section: Hyperspectral (Hs) Imagesmentioning

confidence: 99%