Object-based analysis of multispectral airborne laser scanner data for land cover classification and map updating

Matikainen, Leena; Karila, Kirsi; Hyyppä, Juha; Litkey, Paula; Puttonen, Eetu; Ahokas, Eero

doi:10.1016/j.isprsjprs.2017.04.005

Cited by 118 publications

(93 citation statements)

References 39 publications

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“…Wang et al used two separate ALS systems to acquire dual-wavelength data and found that the use of dual-wavelength data can substantially improve classification accuracy compared to one-wavelength data [41]. More extensive discussions and reference lists on recent multispectral airborne laser scanning point clouds are available from [5][6][7][8]42].…”

Section: Airborne Multispectral Laser Scanningmentioning

confidence: 99%

“…For example, the overall accuracy of the land cover classification results with six classes (building, tree, asphalt, gravel, rocky, low vegetation) can be achieved at the 96% level compared with validation points [8]. An example of multispectral data from the built environment is given in Figure 2.…”

Section: Airborne Multispectral Laser Scanningmentioning

confidence: 99%

“…The position and orientation of mobile, airborne, and UAV systems are solved using a combination of Global Navigation Satellite System (GNSS) and the inertial measurement unit (IMU). Recently, multispectral airborne laser data has also been used to provide point clouds with radiometrically calibrated intensities allowing single-sensor mapping solutions [5][6][7][8]. In addition to LS, point clouds can be obtained with stereophotogrammetry, depth camera based techniques [9], or synthetic aperture radar (SAR) [10].…”

Section: Introductionmentioning

confidence: 99%

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Nationwide Point Cloud—The Future Topographic Core Data

Virtanen

Kukko

Kaartinen

et al. 2017

IJGI

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Topographic databases maintained by national mapping agencies are currently the most common nationwide data sets in geo-information. The application of laser scanning as source data for surveying is increasing. Along with this development, several analysis methods that utilize dense point clouds have been introduced. We present the concept of producing a dense nationwide point cloud, produced from multiple sensors and containing multispectral information, as the national core data for geo-information. Geo-information products, such as digital terrain and elevation models and 3D building models, are produced automatically from these data. We outline the data acquisition, processing, and application of the point cloud. As a national data set, a dense multispectral point cloud could produce significant cost savings via improved automation in mapping and a reduction of overlapping surveying efforts.

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Section: Airborne Multispectral Laser Scanningmentioning

confidence: 99%

Section: Airborne Multispectral Laser Scanningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Nationwide Point Cloud—The Future Topographic Core Data

Virtanen

Kukko

Kaartinen

et al. 2017

IJGI

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“…Each channel produces a separate point cloud. The first studies based on Optech Titan data show the high potential of the data for applications such as land cover classification (e.g., Wichmann et al, 2015;Bakuła et al, 2016;Fernandez-Diaz et al, 2016;Matikainen et al, 2017;Morsy et al, 2017;Teo and Wu, 2017), road mapping , and map updating .…”

Section: Introductionmentioning

confidence: 99%

“…The objective of the present article is to summarise our results and experiences with the multispectral data. The full details of the study have been published in Karila et al (2017) and Matikainen et al (2017).…”

Section: Introductionmentioning

confidence: 99%

Feasibility of Multispectral Airborne Laser Scanning for Land Cover Classification, Road Mapping and Map Updating

Matikainen¹,

Karila²,

Hyyppä³

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This article summarises our first results and experiences on the use of multispectral airborne laser scanner (ALS) data. Optech Titan multispectral ALS data over a large suburban area in Finland were acquired on three different dates in 2015-2016. We investigated the feasibility of the data from the first date for land cover classification and road mapping. Object-based analyses with segmentation and random forests classification were used. The potential of the data for change detection of buildings and roads was also demonstrated. The overall accuracy of land cover classification results with six classes was 96% compared with validation points. The data also showed high potential for road detection, road surface classification and change detection. The multispectral intensity information appeared to be very important for automated classifications. Compared to passive aerial images, the intensity images have interesting advantages, such as the lack of shadows. Currently, we focus on analyses and applications with the multitemporal multispectral data. Important questions include, for example, the potential and challenges of the multitemporal data for change detection.

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