&lt;title&gt;Advanced synthetic image generation models and their application to multi/hyperspectral algorithm development&lt;/title&gt;

Schott, John R.; Brown, Scott; Raqueno, Rolando V.; Gross, Harry N.; Robinson, Gary D.

doi:10.1117/12.339823

Cited by 14 publications

(10 citation statements)

References 1 publication

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“…The last processing module is the retrieval step to compute the ground emissivity and obtain the retrieval error. Other end-to-end simulation approaches use complex synthetic image generation models to include terrain effects and the sensor's modulation transfer function (Schott et al 1999, Bö rner et al 2001. However, for an evaluation of the basic performance of an experimental thermal instrument with broadly defined application fields and lacking any specific scene-dependent requirements an adequate tool has been developed here.…”

Section: Spectral/radiometric Simulationmentioning

confidence: 99%

Spectral and radiometric requirements for the airborne thermal imaging spectrometer ARES

Richter

Müller

Habermeyer

et al. 2005

International Journal of Remote Sensing

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ARES (Airborne Reflective/Emissive Spectrometer) is an airborne imaging spectrometer for remote sensing of land surfaces covering the wavelength regions 0.45-2.45 mm and 8-13 mm with 160 channels. The instrument is being built by Integrated Spectronics, financed by DLR and GFZ, and will be available to the scientific community from 2005 on.This contribution presents the design of the thermal spectrometer covering the 8-13 mm region with 32 channels of 150 nm bandwidth while a separate paper treats the instrument specifications in the solar reflective region. The spectroradiometric design is based on scientific requirements derived from application scenarios comprising vegetation, soils of different compositions, and mineral exploration. The corresponding emissivity spectra are input for a simulation model that calculates at-sensor radiance spectra, resamples them with the channel-specific response functions, adds different amounts of sensor noise to the signal, and performs a retrieval to get the corresponding noisy surface emissivity spectra. The results of the simulation study indicate that a spectral wavelength accuracy of 3 nm and a sensor noise equivalent temperature of 0.05-0.1 K are required for an accurate retrieval of emissivity spectra.

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Section: Spectral/radiometric Simulationmentioning

confidence: 99%

Spectral and radiometric requirements for the airborne thermal imaging spectrometer ARES

Richter

Müller

Habermeyer

et al. 2005

International Journal of Remote Sensing

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“…By defining a threshold on a class similarity function, we prevent new classes present in the target image but not in the source image from being incorrectly classified, allowing for discovery of new classes. We evaluate our technique using state of the art DIRSIG synthetic hyperspectral image data [5].…”

Section: Knowledge Transfer For Hyperspectral Image Classificationmentioning

confidence: 99%

“…We analyze synthetic hyperspectral imagery generated with the RIT Digital Imaging and Remote Sensing Image Generation (DIRSIG) [5] model. We study a subregion of the the RIT "Megascene" [7], with 400x400 pixels at 4m/pixel resolution.…”

Section: Dirsig Synthetic Hyperspectral Imagerymentioning

confidence: 99%

Using spatial correspondences for hyperspectral knowledge transfer: Evaluation on synthetic data

Bue

Merényi

2010

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing

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We describe a proof of concept for class knowledge transfer from a labeled hyperspectral image to an unlabeled image, captured with a different (hyper-/multi-spectral) sensor, when the spatial extents of the images partially overlap. By defining a set of spatio-spectral correspondences between the labeled source image and the unlabeled target image, we create a mapping between the images we can use to propagate labels from the source to the target image. This mapping allows us to classify the target image using the source labels without manually defining training labels in the target image. We evaluate the technique using state of the art synthetic hyperspectral imagery.

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“…Therefore, a careful modeling of each contribution is required to accurately calculate TOA radiance. A number of comprehensive simulation tools coupling surface and atmosphere radiative transfer processes have been introduced [18][19][20][21][22][23][24][25][26][27][28]. However, some of these studies are often simplified through various assumptions such as flat terrain, Lambertian assumption for the surface reflectance and some others are more accurate but at very high computational cost that limits their application.…”

Section: Introductionmentioning

confidence: 99%

Modeling Top of Atmosphere Radiance over Heterogeneous Non-Lambertian Rugged Terrain

et al. 2015

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Topography affects the fraction of direct and diffuse radiation received on a pixel and changes the sun-target-sensor geometry, resulting in variations in the observed radiance. Retrieval of surface-atmosphere properties from top of atmosphere radiance may need to account for topographic effects. This study investigates how such effects can be taken into account for top of atmosphere radiance modeling. In this paper, a system for top of atmosphere radiance modeling over heterogeneous non-Lambertian rugged terrain through radiative transfer modeling is presented. The paper proposes an extension of "the four-stream radiative transfer theory" , 2007) mainly aimed at representing topography-induced contributions to the top of atmosphere radiance modeling. A detailed account for BRDF effects, adjacency effects and topography effects on the radiance modeling is given, in which sky-view factor and non-Lambertian reflected radiance from adjacent slopes are modeled precisely. The paper also provides a new formulation to derive the atmospheric coefficients from MODTRAN with only two model runs, to make it more computationally efficient and also avoiding the use of zero surface albedo as used in the four-stream radiative transfer theory. The modeling begins with four surface reflectance factors calculated by the Soil-Leaf-Canopy radiative transfer model SLC at the top of canopy and propagates them through the effects of the atmosphere, which is explained by six atmospheric coefficients, derived from MODTRAN radiative OPEN ACCESSRemote Sens. 2015, 7 8020 transfer code. The top of the atmosphere radiance is then convolved with the sensor characteristics to generate sensor-like radiance. Using a composite dataset, it has been shown that neglecting sky view factor and/or terrain reflected radiance can cause uncertainty in the forward TOA radiance modeling up to 5 (mW/m 2 ·sr·nm). It has also been shown that this level of uncertainty can be translated into an over/underestimation of more than 0.5 in LAI (or 0.07 in fCover) in variable retrieval.

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<title>Advanced synthetic image generation models and their application to multi/hyperspectral algorithm development</title>

Cited by 14 publications

References 1 publication

Spectral and radiometric requirements for the airborne thermal imaging spectrometer ARES

Spectral and radiometric requirements for the airborne thermal imaging spectrometer ARES

Using spatial correspondences for hyperspectral knowledge transfer: Evaluation on synthetic data

Modeling Top of Atmosphere Radiance over Heterogeneous Non-Lambertian Rugged Terrain

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