scholarly journals Machine Learning Comparison between WorldView-2 and QuickBird-2-Simulated Imagery Regarding Object-Based Urban Land Cover Classification

2011 ◽  
Vol 3 (10) ◽  
pp. 2263-2282 ◽  
Author(s):  
Tessio Novack ◽  
Thomas Esch ◽  
Hermann Kux ◽  
Uwe Stilla
Keyword(s):  
Machine Learning ◽  
Land Cover ◽  
Land Cover Classification ◽  
Urban Land ◽  
Urban Land Cover ◽  
Object Based

Object-based image analysis for urban land cover classification in the city of Campinas - SP, Brazil

2015 ◽  
Author(s):  
David G. M. Franca ◽  
Rodolfo G. Lotte ◽  
Claudia M. de Almeida ◽  
Sacha M. O. Siani ◽  
Thales S. Korting ◽  
...  
Keyword(s):  
Image Analysis ◽  
Land Cover ◽  
Land Cover Classification ◽  
Urban Land ◽  
Object Based Image Analysis ◽  
Urban Land Cover ◽  
Object Based ◽  
The City

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

2021 ◽  
Vol 13 (17) ◽  
pp. 3393
Author(s):  
Agnieszka Kuras ◽  
Maximilian Brell ◽  
Jonathan Rizzi ◽  
Ingunn Burud
Keyword(s):  
Machine Learning ◽  
Land Cover ◽  
Land Cover Classification ◽  
Urban Land ◽  
Urban Environments ◽  
Machine Learning Algorithms ◽  
Urban Structures ◽  
Urban Land Cover ◽  
Technological Advances ◽  
Remote Sensing Systems

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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