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Land Use Classification from Lidar Data and Ortho‐Images in a Rural Area
Author(s) -
Buján Sandra,
GonzálezFerreiro Eduardo,
ReyesBueno Fabián,
BarreiroFernández Laura,
Crecente Rafael,
Miranda David
Publication year - 2012
Publication title -
the photogrammetric record
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.638
H-Index - 51
eISSN - 1477-9730
pISSN - 0031-868X
DOI - 10.1111/j.1477-9730.2012.00698.x
Subject(s) - lidar , remote sensing , correctness , ground truth , computer science , contextual image classification , index (typography) , cohen's kappa , geography , artificial intelligence , image (mathematics) , machine learning , world wide web , programming language
Abstract Obtaining information on the distribution of rural landscape types is an active research topic within Spanish rural studies. This paper presents a new hierarchical object‐based classification method for the automatic detection of various land use classes in a rural area, combining lidar data and aerial images. In view of the upcoming availability of low‐density lidar data (0·5 pulses/m 2 ) for most of the territory of Spain, this paper assesses the feasibility and accuracy of the proposed method for various lidar data densities. Such an assessment was conducted using two approaches: firstly, based on the final classification, which produced an overall accuracy over 96% and a kappa index above 0·95 for the combinations of the aerial image and lidar data‐sets with four different densities; and secondly, based solely on the areas classified as buildings. In the second approach, the accuracy of the classification for building detection at pixel and object level was assessed. The object‐oriented classification of buildings produced an index of correctness of over 99% and an index of completeness of about 95%. The results reveal a high agreement between classification and ground truth data.