Airborne Dual-Wavelength LiDAR Data for Classifying  Land Cover

Wang, Cheng Kai; Tseng, Yi Hsing; Chu, Hsin

doi:10.3390/rs6010700

Cited by 44 publications

(31 citation statements)

References 38 publications

(15 reference statements)

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“…In Briese et al (2013) multiwavelength LiDAR amplitudes are used for archeological prospection on grass vegetated terrain. A classification study of land cover (roads and gravel, bare soil, low vegetation, high vegetation, roofs, and water bodies) is presented by Wang et al (2014). They combine surveys from an Optech ALTM Pegasus HD400 (1,064 nm) and a Riegl LMSQ680i (1,550 nm).…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Potential of Multispectral Airborne Lidar for Topographic Mapping and Land Cover Classification

Wichmann¹,

Bremer

Lindenberger³

et al. 2015

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Recently multispectral LiDAR became a promising research field for enhanced LiDAR classification workflows and e.g. the assessment of vegetation health. Current analyses on multispectral LiDAR are mainly based on experimental setups, which are often limited transferable to operational tasks. In late 2014 Optech Inc. announced the first commercially available multispectral LiDAR system for airborne topographic mapping. The combined system makes synchronic multispectral LiDAR measurements possible, solving time shift problems of experimental acquisitions. This paper presents an explorative analysis of the first airborne collected data with focus on class specific spectral signatures. Spectral patterns are used for a classification approach, which is evaluated in comparison to a manual reference classification. Typical spectral patterns comparable to optical imagery could be observed for homogeneous and planar surfaces. For rough and volumetric objects such as trees, the spectral signature becomes biased by signal modification due to multi return effects. However, we show that this first flight data set is suitable for conventional geometrical classification and mapping procedures. Additional classes such as sealed and unsealed ground can be separated with high classification accuracies. For vegetation classification the distinction of species and health classes is possible.

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Section: Introductionmentioning

confidence: 99%

Evaluating the Potential of Multispectral Airborne Lidar for Topographic Mapping and Land Cover Classification

Wichmann¹,

Bremer

Lindenberger³

et al. 2015

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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show abstract

“…It can collect totally * Corresponding author seamless data from land, the coastal zone and the seafloor down to a certain depth (Leica HawkEye III, 2015). Wang et al (2014) proposed a dual-wavelength LiDAR system by integrating an Optech ALTM Pegasus HD400 (1064 nm) and a Riegl LMSQ680i (1550 nm) to combine surveys for a classification study of land cover. Based on the features of Lidar data such as dual-wavelength amplitude, echo width and surface height, a support vector machine was used to classify six types of suburban land cover: roads and gravel, bare soil, low vegetation, high vegetation, roofs, and water bodies.…”

Section: Introductionmentioning

confidence: 99%

3d Land Cover Classification Based on Multispectral Lidar Point Clouds

Zou¹,

Zhao²,

Li³

et al. 2016

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

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ABSTRACT:Multispectral Lidar System can emit simultaneous laser pulses at the different wavelengths. The reflected multispectral energy is captured through a receiver of the sensor, and the return signal together with the position and orientation information of sensor is recorded. These recorded data are solved with GNSS/IMU data for further post-processing, forming high density multispectral 3D point clouds. As the first commercial multispectral airborne Lidar sensor, Optech Titan system is capable of collecting point clouds data from all three channels at 532nm visible (Green), at 1064 nm near infrared (NIR) and at 1550nm intermediate infrared (IR). It has become a new source of data for 3D land cover classification. The paper presents an Object Based Image Analysis (OBIA) approach to only use multispectral Lidar point clouds datasets for 3D land cover classification. The approach consists of three steps. Firstly, multispectral intensity images are segmented into image objects on the basis of multi-resolution segmentation integrating different scale parameters. Secondly, intensity objects are classified into nine categories by using the customized features of classification indexes and a combination the multispectral reflectance with the vertical distribution of object features. Finally, accuracy assessment is conducted via comparing random reference samples points from google imagery tiles with the classification results. The classification results show higher overall accuracy for most of the land cover types. Over 90% of overall accuracy is achieved via using multispectral Lidar point clouds for 3D land cover classification.

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“…However, one application for which it can excel is providing high resolution active multispectral data derived from lidar intensity. In theory and in practice, multispectral lidar intensity can be used for many applications, including return/target classification [28,29]; individual tree identification, parameterization and classification [30]; water/land interface identification; and archaeological feature detection [31], among many others. Several years will go by before scientists in each of the above-mentioned fields will have an opportunity to assess the utility of the Titan multispectral lidar datasets as compared to what has been theorized or experimented so far.…”

Section: Multispectral Capabilitiesmentioning

confidence: 99%

Capability Assessment and Performance Metrics for the Titan Multispectral Mapping Lidar

et al. 2016

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Abstract:In this paper we present a description of a new multispectral airborne mapping light detection and ranging (lidar) along with performance results obtained from two years of data collection and test campaigns. The Titan multiwave lidar is manufactured by Teledyne Optech Inc. (Toronto, ON, Canada) and emits laser pulses in the 1550, 1064 and 532 nm wavelengths simultaneously through a single oscillating mirror scanner at pulse repetition frequencies (PRF) that range from 50 to 300 kHz per wavelength (max combined PRF of 900 kHz). The Titan system can perform simultaneous mapping in terrestrial and very shallow water environments and its multispectral capability enables new applications, such as the production of false color active imagery derived from the lidar return intensities and the automated classification of target and land covers. Field tests and mapping projects performed over the past two years demonstrate capabilities to classify five land covers in urban environments with an accuracy of 90%, map bathymetry under more than 15 m of water, and map thick vegetation canopies at sub-meter vertical resolutions. In addition to its multispectral and performance characteristics, the Titan system is designed with several redundancies and diversity schemes that have proven to be beneficial for both operations and the improvement of data quality.

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Airborne Dual-Wavelength LiDAR Data for Classifying Land Cover

Cited by 44 publications

References 38 publications

Evaluating the Potential of Multispectral Airborne Lidar for Topographic Mapping and Land Cover Classification

Evaluating the Potential of Multispectral Airborne Lidar for Topographic Mapping and Land Cover Classification

3d Land Cover Classification Based on Multispectral Lidar Point Clouds

Capability Assessment and Performance Metrics for the Titan Multispectral Mapping Lidar

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