Hyperspectral and Lidar Intensity Data Fusion: A Framework for the Rigorous Correction of Illumination, Anisotropic Effects, and Cross Calibration

Brell, Maximilian; Segl, Karl; Guanter, Luis; Bookhagen, Bodo

doi:10.1109/tgrs.2017.2654516

Cited by 45 publications

(38 citation statements)

References 29 publications

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“…This is not only due to the limited availability of suited models: for natural objects such as forests, the assumption of a continuous geometric surface does not hold true, which often results in erroneous mapping of shadow borders. With the use of LIDAR techniques, this may be solved in the future [34].…”

Section: Cast Shadow Detection Methodsmentioning

confidence: 99%

Cast Shadow Detection to Quantify the Aerosol Optical Thickness for Atmospheric Correction of High Spatial Resolution Optical Imagery

Schläpfer¹,

Hueni

Richter

2018

Remote Sensing

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Abstract:The atmospheric correction of optical remote sensing data requires the determination of aerosol and gas optical properties. A method is presented which allows the detection of the aerosol scattering effects from optical remote sensing data at spatial sampling intervals below 5 m in cloud-free situations from cast shadow pixels. The derived aerosol optical thickness distribution is used for improved atmospheric compensation. In a first step, a novel spectral cast shadow detection algorithm determines the shadow areas using spectral indices. Evaluation of the cast shadow masks shows an overall classification accuracy on an 80% level. Using the such derived shadow map, the ATCOR atmospheric compensation method is iteratively applied on the shadow areas in order to find the optimum aerosol amount. The aerosol optical thickness is found by analyzing the physical atmospheric correction of fully shaded pixels in comparison to directly illuminated areas. The shadow based aerosol optical thickness estimation method (SHAOT) is tested on airborne imaging spectroscopy data as well as on photogrammetric data. The accuracy of the reflectance values from atmospheric correction using the such derived aerosol optical thickness could be improved from 3-4% to a level of better than 2% in reflectance for the investigated test cases.

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Section: Cast Shadow Detection Methodsmentioning

confidence: 99%

Cast Shadow Detection to Quantify the Aerosol Optical Thickness for Atmospheric Correction of High Spatial Resolution Optical Imagery

Schläpfer¹,

Hueni

Richter

2018

Remote Sensing

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“…Additional to the elevation model, more advanced approaches use both the IS and ALS intensity information in an overlapping wavelength domain (Fig. 12a) (Brell et al, 2016(Brell et al, , 2017. This strategy enhances the synergistic use especially for the accurate co-registration of the two sensors (Brell et al, 2016).…”

Section: Synergies Of Is With Lidar Datamentioning

confidence: 99%

Synergies of Spaceborne Imaging Spectroscopy with Other Remote Sensing Approaches

et al. 2018

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Imaging spectroscopy (IS), also commonly known as hyperspectral remote sensing, is a powerful remote sensing technique for the monitoring of the Earth's surface and atmosphere. Pixels in optical hyperspectral images consist of continuous reflectance spectra formed by hundreds of narrow spectral channels, allowing an accurate representation of the surface composition through spectroscopic techniques. However, technical constraints in the definition of imaging spectrometers make spectral coverage and resolution to be usually traded by spatial resolution and swath width, as opposed to optical multispectral (MS) systems typically designed to maximize spatial and/or temporal resolution. This complementarity suggests that a synergistic exploitation of spaceborne IS and MS data would be an optimal way to fulfill those remote sensing applications requiring not only high spatial and temporal resolution data, but also rich spectral information. On the other hand, IS has been shown to yield a strong synergistic potential with non-optical remote sensing methods, such as thermal infrared (TIR) and light detection and ranging (LiDAR). In this contribution we review theoretical and methodological aspects of potential synergies between optical IS and other remote sensing techniques. The focus is put on the evaluation of synergies between spaceborne optical IS and MS systems because of the expected availability of the two types of data in the next years. Short reviews of potential synergies of IS with TIR and LiDAR measurements are also provided.

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“…In analogy to ALS, they may be partly summarized in another, HSI-specific, calibration constant (Brell et al, 2017).…”

Section: Theorymentioning

confidence: 99%

“…Thorough radiometric calibration of full-waveform ALS data allows for the derivation of a surface or bottom-of-atmosphere (BOA) reflectance value. In order to fuse HSI and ALS intensity and to perform cross-calibration, such surfaces are required to act as homogeneous targets and Lambertian (=diffuse) reflectors (Brell et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Assessment of Bottom-of-Atmosphere Reflectance in Lidar Data As Reference for Hyperspectral Imagery

Roncat

Pfeifer

Briese³

2017

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

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ABSTRACT:While airborne lidar has confirmed its leading role in delivering high-resolution 3D topographic information during the last decade, its radiometric potential has not yet been fully exploited. However, with the increasing availability of commercial lidar systems which (a) make use of full-waveform information and (b) operate at several wavelengths simultaneously, this potential is increasing as well. Radiometric calibration of the full-waveform information mentioned before allows for the derivation of physical target surface parameters such as the backscatter coefficient and a diffuse reflectance value at bottom of atmosphere (BOA), i.e. the target surface. With lidar being an active remote sensing technique, these parameters can be derived from lidar data itself, accompanied by the measurement or estimation of reference data for diffuse reflectance. In contrast to this, such a radiometric calibration for passive hyperspectral imagery (HSI) requires the knowledge and/or estimation of much more unknowns. However, in case of corresponding wavelength(s) radiometrically calibrated lidar datasets can deliver an areawide reference for BOA reflectance. This paper presents criteria to check where the assumption of diffuse BOA reflectance behaviour is fulfilled and how these criteria are assessed in lidar data; the assessment is illustrated by an extended lidar dataset. Moreover, for this lidar dataset and an HSI dataset recorded over the same area, the corresponding reflectance values are compared for different surface types.

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Hyperspectral and Lidar Intensity Data Fusion: A Framework for the Rigorous Correction of Illumination, Anisotropic Effects, and Cross Calibration

Cited by 45 publications

References 29 publications

Cast Shadow Detection to Quantify the Aerosol Optical Thickness for Atmospheric Correction of High Spatial Resolution Optical Imagery

Cast Shadow Detection to Quantify the Aerosol Optical Thickness for Atmospheric Correction of High Spatial Resolution Optical Imagery

Synergies of Spaceborne Imaging Spectroscopy with Other Remote Sensing Approaches

Assessment of Bottom-of-Atmosphere Reflectance in Lidar Data As Reference for Hyperspectral Imagery

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