Artificial target detection with a hyperspectral LiDAR over 26-h measurement

Puttonen, Eetu; Hakala, Teemu; Nevalainen, Olli; Kaasalainen, Sanna; Krooks, Anssi; Karjalainen, Mika; Anttila, Kati

doi:10.1117/1.oe.54.1.013105

Cited by 60 publications

(40 citation statements)

References 52 publications

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“…The approach is a laboratory setup for scanning single leafs. Puttonen et al (2015) present a study on an outdoor scanning experiment with different high vegetation species. They calculate several vegetation indices relating them to leaf area index, chlorophyll in leafs and biomass estimates.…”

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

“…Experiences on this field have been obtained, for example, from studies with a hyperspectral laser scanner developed at the FGI (Chen et al, 2010;Hakala et al, 2012). The system is a terrestrial one and has been successfully applied for vegetation analyses and separation of man-made targets from natural ones (e.g., Puttonen et al, 2015). Phennigbauer and Ullrich (2011) discussed the development and potential of multi-wavelength ALS systems.…”

Section: Introductionmentioning

confidence: 99%

“…They found that the use of dualwavelength data can substantially improve classification accuracy compared to one-wavelength data. More extensive discussions and reference lists on these earlier developments of multispectral laser scanning can be found, for example, in Puttonen et al (2015) and Wichmann et al (2015).…”

Section: Introductionmentioning

confidence: 99%

Multispectral Airborne Laser Scanning for Automated Map Updating

Matikainen¹,

Hyyppä²,

Litkey³

2016

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

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ABSTRACT:During the last 20 years, airborne laser scanning (ALS), often combined with multispectral information from aerial images, has shown its high feasibility for automated mapping processes. Recently, the first multispectral airborne laser scanners have been launched, and multispectral information is for the first time directly available for 3D ALS point clouds. This article discusses the potential of this new single-sensor technology in map updating, especially in automated object detection and change detection. For our study, Optech Titan multispectral ALS data over a suburban area in Finland were acquired. Results from a random forests analysis suggest that the multispectral intensity information is useful for land cover classification, also when considering ground surface objects and classes, such as roads. An out-of-bag estimate for classification error was about 3% for separating classes asphalt, gravel, rocky areas and low vegetation from each other. For buildings and trees, it was under 1%. According to feature importance analyses, multispectral features based on several channels were more useful that those based on one channel. Automatic change detection utilizing the new multispectral ALS data, an old digital surface model (DSM) and old building vectors was also demonstrated. Overall, our first analyses suggest that the new data are very promising for further increasing the automation level in mapping. The multispectral ALS technology is independent of external illumination conditions, and intensity images produced from the data do not include shadows. These are significant advantages when the development of automated classification and change detection procedures is considered.

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“…This HSL system possesses the high 3-dimensional and abundant spectral properties capability, which has been applied in many practical fields. Especially in the vegetation biochemical parameter estimation, it has showed considerable advantages and potential (Hakala et al, 2012;Li et al, 2014;Puttonen et al, 2015;Nevalainen et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Potential Application of Novel Hyperspectral Lidar for Monitoring Crops Nitrogen Stress

Shi¹,

Gong²,

Li³

et al. 2016

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

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ABSTRACT:Precision agriculture has always been the research hotspot around the world. And the optimization of nitrogen fertilization for crops is the core concerns. It is not only to improve the productivity of crops but also to avoid the environmental risks caused by overfertilization. Therefore, accurate estimation of nitrogen status is crucial for determining an nitrogen recommendation. Remote sensing techniques have been widely used to monitor crops for years, and they could offer estimations for stress status diagnosis through obtaining vertical structure parameters and spectral reflectance properties of crops. As an active remote sensing technology, lidar is particularly attractive for 3-dimensional information at a high point density. It has unique edges in obtaining vertical structure parameters of crops. However, capability of spectral reflectance properties is what the current lidar technology lacks because of single wavelength detection. To solve this problem, the concept of novel hyperspectral lidar (HSL), which combines the advantages of hyperspectal reflectance with high 3-dimensional capability of lidar, was proposed in our study. The design of instrument was described in detail. A broadband laser pulse was emitted and reflectance spectrum with 32 channels could be detected. Furthermore, the experiment was carried out by the novel HSL system to testify the potential application for monitoring nitrogen stress. Rice under different levels of nitrogen fertilization in central China were selected as the object of study, and four levels of nitrogen fertilization (N1-N4) were divided. With the detection of novel lidar system, high precision structure parameters of crops could be provided. Meanwhile, spectral reflectance properties in 32 wavebands were also obtained. The high precision structure parameters could be used to evaluate the stress status of crops. And abundant spectral information in 32 wavebands could improve the capacity of lidar system significantly. The results demonstrate that it is more effective for HSL system to distinguish different levels of nitrogen fertilization. Overall, HSL allows for probing not only high precision structure parameters but also spectral reflectance properties of crops. Compared with other approaches, the novel HSL has the potential to provide more comprehensive information of crops which can be assessed remotely in the application of precision agriculture.

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Artificial target detection with a hyperspectral LiDAR over 26-h measurement

Cited by 60 publications

References 52 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

Multispectral Airborne Laser Scanning for Automated Map Updating

Potential Application of Novel Hyperspectral Lidar for Monitoring Crops Nitrogen Stress

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