Correction of Building Height Effect Using LIDAR and GPS

Sohn, Hong-Gyoo; Yun, Kong-Hyun; Kim, Gi Hong; Park, Hyo Sun

doi:10.1007/11557654_121

Cited by 2 publications

(1 citation statement)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, passive remote sensors, such as HS, are sensitive to atmospheric conditions and illumination, whereas lidar as an active sensor is less sensitive to these factors. This property of lidar enables, e.g., a physical correction of shadow and illumination purposes when combined with HS data [20][21][22][23][24][25] and intensity measurement for urban land cover mapping in shaded areas [26]. Regardless of the spatial and spectral resolution of airborne-based HS sensors, urban environments are characterized by spectral ambiguity and reduced spectral value under the shadow caused by topography changes, buildings, and trees, which can be overcome by adding lidar data as presented by [27].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

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.

show abstract