A comparison of MCScene and CameoSim simulations of a real scene

Pereira, W. E.; Richtsmeier, Steven C.; Carr, S.B.; Kharabash, Sergey; Brady, Andrew

doi:10.1109/whispers.2014.8077572

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…Terrain information can be imported from United States Geological Surveys (USGS) digital elevation maps. Surface reflectance or emissivity/temperature maps can be derived from collected imagery, thus incorporating natural spectral and spatial texturing into a simulation [17][18][19][20].…”

Section: Monte-carlo Scene (Mcscene)mentioning

confidence: 99%

“…It has been shown that the density of air D(z) is exponentially reduced above z d elevation; (2R × 2R) ROI. We incorporateρ bK into Equation (2) by interpolation, therefore ρ u is calculated for ρ b = 0.0 andρ b = 1.0 from Equation 10, and it is denoted by ρ u0 and ρ u1 , respectively which is given in Equation (19). Interpolation between zero and one is performed in these experiments due to large test scene and memory constraints which may incur interpolation errors, therefore it is recommended to calculate ρ u from MODTRAN at more intermediate points between zero and one.…”

Section: Proposed Adjacency Models For Chimes Simulatormentioning

confidence: 99%

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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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Section: Monte-carlo Scene (Mcscene)mentioning

confidence: 99%

Section: Proposed Adjacency Models For Chimes Simulatormentioning

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 principle, this can be achieved through repeated costly and labour intensive experimental trials until the desire result is obtained. Alternatively, this can be accomplished through effective hyperspectral scene simulation technique, which is capable to reconstruct the scene and at the same time to “inject” foreign materials into the environment [ 2 , 3 ]. The ultimate usefulness of a scene simulator is its ability to simulate the scene faithfully, such that the spectral and spatial content of the simulation is as realistic as possible.…”

Section: Introductionmentioning

confidence: 99%

Endmember Learning with K-Means through SCD Model in Hyperspectral Scene Reconstructions

Chatterjee

Yuen

2019

J. Imaging

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This paper proposes a simple yet effective method for improving the efficiency of sparse coding dictionary learning (DL) with an implication of enhancing the ultimate usefulness of compressive sensing (CS) technology for practical applications, such as in hyperspectral imaging (HSI) scene reconstruction. CS is the technique which allows sparse signals to be decomposed into a sparse representation “a” of a dictionary D u . The goodness of the learnt dictionary has direct impacts on the quality of the end results, e.g., in the HSI scene reconstructions. This paper proposes the construction of a concise and comprehensive dictionary by using the cluster centres of the input dataset, and then a greedy approach is adopted to learn all elements within this dictionary. The proposed method consists of an unsupervised clustering algorithm (K-Means), and it is then coupled with an advanced sparse coding dictionary (SCD) method such as the basis pursuit algorithm (orthogonal matching pursuit, OMP) for the dictionary learning. The effectiveness of the proposed K-Means Sparse Coding Dictionary (KMSCD) is illustrated through the reconstructions of several publicly available HSI scenes. The results have shown that the proposed KMSCD achieves ~40% greater accuracy, 5 times faster convergence and is twice as robust as that of the classic Spare Coding Dictionary (C-SCD) method that adopts random sampling of data for the dictionary learning. Over the five data sets that have been employed in this study, it is seen that the proposed KMSCD is capable of reconstructing these scenes with mean accuracies of approximately 20–500% better than all competing algorithms adopted in this work. Furthermore, the reconstruction efficiency of trace materials in the scene has been assessed: it is shown that the KMSCD is capable of recovering ~12% better than that of the C-SCD. These results suggest that the proposed DL using a simple clustering method for the construction of the dictionary has been shown to enhance the scene reconstruction substantially. When the proposed KMSCD is incorporated with the Fast non-negative orthogonal matching pursuit (FNNOMP) to constrain the maximum number of materials to coexist in a pixel to four, experiments have shown that it achieves approximately ten times better than that constrained by using the widely employed TMM algorithm. This may suggest that the proposed DL method using KMSCD and together with the FNNOMP will be more suitable to be the material allocation module of HSI scene simulators like the CameoSim package.

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Limb-viewing hyperspectral image simulation based on a polygonal earth cross-section (PEX) model

Berk

Richtsmeier

Sundberg

2017

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

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A comparison of MCScene and CameoSim simulations of a real scene

Cited by 4 publications

References 6 publications

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

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

Endmember Learning with K-Means through SCD Model in Hyperspectral Scene Reconstructions

Limb-viewing hyperspectral image simulation based on a polygonal earth cross-section (PEX) model

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