DIRSIG5: Next-Generation Remote Sensing Data and Image Simulation Framework

Goodenough, Adam A.; Brown, Scott

doi:10.1109/jstars.2017.2758964

Cited by 51 publications

(15 citation statements)

References 25 publications

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“…Some of the measured reflectances were validated using DIRSIG5 software [31]. DIRSIG5 is a physically-based Monte Carlo path tracer-i.e., DIRSIG5 estimates the radiance incident upon a virtual detector array by tracing light transport paths randomly through a virtual scene, wherein triangles constitute virtual surfaces, which are characterized by energy-conserving light scattering models.…”

Section: Dirsig5mentioning

confidence: 99%

Inherent Reflectance Variability of Vegetation

2019

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With the inception of small unmanned aircraft systems (sUAS), remotely sensed images have been captured much closer to the ground, which has meant better resolution and smaller ground sample distances (GSDs). This has provided the precision agriculture community with the ability to analyze individual plants, and in certain cases, individual leaves on those plants. This has also allowed for a dramatic increase in data acquisition for agricultural analysis. Because satellite and manned aircraft remote sensing data collections had larger GSDs, self-shadowing was not seen as an issue for agricultural remote sensing. However, sUAS are able to image these shadows which can cause issues in data analysis. This paper investigates the inherent reflectance variability of vegetation by analyzing six Coneflower plants, as a surrogate for other cash crops, across different variables. These plants were measured under different forecasts (cloudy and sunny), at different times (08:00 a.m., 09:00 a.m., 10:00 a.m., 11:00 a.m. and 12:00 p.m.), and at different GSDs (2, 4 and 8 cm) using a field portable spectroradiometer (ASD Field Spec). In addition, a leafclip spectrometer was utilized to measure individual leaves on each plant in a controlled lab environment. These spectra were analyzed to determine if there was any significant difference in the health of the various plants measured. Finally, a MicaSense RedEdge-3 multispectral camera was utilized to capture images of the plants every hour to analyze the variability produced by a sensor designed for agricultural remote sensing. The RedEdge-3 was held stationary at 1.5 m above the plants while collecting all images, which produced a GSD of 0.1 cm/pixel. To produce 2, 4, and 8 cm GSD, the MicaSense RedEdge-3 would need to be at an altitude of 30.5 m, 61 m and 122 m respectively. This study did not take background effects into consideration for either the ASD or MicaSense. Results showed that GSD produced a statistically significant difference (p < 0.001) in Normalized Difference Vegetation Index (NDVI, a commonly used metric to determine vegetation health), R 2 values demonstrated a low correlation between time of day and NDVI, and a one-way ANOVA test showed no statistically significant difference in the NDVI computed from the leafclip probe (p-value of 0.018). Ultimately, it was determined that the best condition for measuring vegetation reflectance was on cloudy days near noon. Sunny days produced self-shadowing on the plants which increased the variability of the measured reflectance values (higher standard deviations in all five RedEdge-3 channels), and the shadowing of the plants decreased as time approached noon. This high reflectance variability in the coneflower plants made it difficult to accurately measure the NDVI.

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Section: Dirsig5mentioning

confidence: 99%

Inherent Reflectance Variability of Vegetation

2019

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“…Digital imaging and remote sensing image generation (DIRSIG) uses a ray-tracing approach that accounts for the random nature of light scattering and accounts for various phenomena including adjacency effects. Using the DIRSIG model, we designed a scene and replicated the conditions in which the spectral images would be collected to generate images that were used to assess subpixel target detection performance [33]. This model accounted for each imaging chain component similar to FASSP, using MODTRAN to model the atmosphere and illumination conditions, sensor characteristics with collection geometry, and spectral information of the materials in the scene.…”

Section: ) Fasspmentioning

confidence: 99%

Optimizing Requirements for a Compact Spaceborne Adaptive Spectral Imaging System in Subpixel Target Detection Applications

Han

Kerekes

Higbee

et al. 2020

IEEE J. Miniat. Air Space Syst.

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We developed a process to provide design recommendations for compact spaceborne spectral imaging systems with adaptive band selection capabilities. Our focus application was subpixel target detection, and we analyzed a set of mission scenarios to find relationships in detection performance between selected parameters of interest. We used an analytic model to predict performance and generate trade curves, then simulated a scene to analyze potential operational effects on performance for the selected target and background combinations. Using these models, we predicted and assessed each scenario to provide recommendations for mission feasibility and system design. The parameters we selected for analysis were target fill fraction, noise, number of bands, and scene complexity to find critical points in the trade space and reach a set of recommendations. We examined the operational effects by simulating a realistic scenario and ensuring key real-world phenomena were captured within the spectral images. Our results produced recommendations for each mission and provided a proof of concept for a process to analyze designs of miniature spaceborne imaging systems.

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“…Its components include bi-directional reflectance distribution function (BRDF) predictions of a surface, time and material dependent surface temperature predictions, to the dynamic viewing geometry of scanning imaging instruments on agile ground, airborne and space-based platforms [4][5][6][7]. In a recent update called DIRSIG5 [8] Metropolis Light Transport (MLT) based path tracing has been used. Atmospheric processing can be performed by both MODTRAN5 and MODTRAN6 [9].…”

Section: Digital Imaging and Remote Sensing Image Generation (Dirsig)mentioning

confidence: 99%

“…The upward surface flux that is reflected by the atmosphere back to the ground and then reflected by the groundρ b upwards. Since this phenomena occur multiple times before it is finally scattered into the LoS of the sensor, therefore it takes the form of power series as given in Equation (8).…”

Section: Dirsigmentioning

confidence: 99%

An End-to-End Hyperspectral Scene Simulator with Alternate Adjacency Effect Models and Its Comparison with CameoSim

et al. 2019

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In this research, we developed a new rendering-based end to end Hyperspectral scene simulator CHIMES (Cranfield Hyperspectral Image Modelling and Evaluation System), which generates nadir images of passively illuminated 3-D outdoor scenes in Visible, Near Infrared (NIR) and Short-Wave Infrared (SWIR) regions, ranging from 360 nm to 2520 nm. MODTRAN TM (MODerate resolution TRANsmission), is used to generate the sky-dome environment map which includes sun and sky radiance along with the polarisation effect of the sky due to Rayleigh scattering. Moreover, we perform path tracing and implement ray interaction with medium and volumetric backscattering at rendering time to model the adjacency effect. We propose two variants of adjacency models, the first one incorporates a single spectral albedo as the averaged background of the scene, this model is called the Background One-Spectra Adjacency Effect Model (BOAEM), which is a CameoSim like model created for performance comparison. The second model calculates background albedo from a pixel’s neighbourhood, whose size depends on the air volume between sensor and target, and differential air density up to sensor altitude. Average background reflectance of all neighbourhood pixel is computed at rendering time for estimating the total upwelled scattered radiance, by volumetric scattering. This model is termed the Texture-Spectra Incorporated Adjacency Effect Model (TIAEM). Moreover, for estimating the underlying atmospheric condition MODTRAN is run with varying aerosol optical thickness and its total ground reflected radiance (TGRR) is compared with TGRR of known in-scene material. The Goodness of fit is evaluated in each iteration, and MODTRAN’s output with the best fit is selected. We perform a tri-modal validation of simulators on a real hyperspectral scene by varying atmospheric condition, terrain surface models and proposed variants of adjacency models. We compared results of our model with Lockheed Martin’s well-established scene simulator CameoSim and acquired Ground Truth (GT) by Hyspex cameras. In clear-sky conditions, both models of CHIMES and CameoSim are in close agreement, however, in searched overcast conditions CHIMES BOAEM is shown to perform better than CameoSim in terms of ℓ 1 -norm error of the whole scene with respect to GT. TIAEM produces better radiance shape and covariance of background statistics with respect to Ground Truth (GT), which is key to good target detection performance. We also report that the results of CameoSim have a many-fold higher error for the same scene when the flat surface terrain is replaced with a Digital Elevation Model (DEM) based rugged one.

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DIRSIG5: Next-Generation Remote Sensing Data and Image Simulation Framework

Cited by 51 publications

References 25 publications

Inherent Reflectance Variability of Vegetation

Inherent Reflectance Variability of Vegetation

Optimizing Requirements for a Compact Spaceborne Adaptive Spectral Imaging System in Subpixel Target Detection Applications

An End-to-End Hyperspectral Scene Simulator with Alternate Adjacency Effect Models and Its Comparison with CameoSim

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