MODTRAN on supercomputers and parallel computers

Wang, P.; Liu, Karen Y.; Cwik, T.; Green, Robert C.

doi:10.1016/s0167-8191(01)00128-4

Cited by 33 publications

(11 citation statements)

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“…At the atmosphere scale, MODTRAN5 [24], the moderate resolution transmittance code, is one of the most widely used radiative transfer codes in the atmospheric community [59]. MODTRAN solves the radiative transfer equation by including the effects of molecular and particulate absorption/emission and scattering, surface reflections and emission, solar/lunar illumination, and spherical refraction.…”

Section: Modtran5mentioning

confidence: 99%

Emulation of Leaf, Canopy and Atmosphere Radiative Transfer Models for Fast Global Sensitivity Analysis

et al. 2016

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Abstract:Physically-based radiative transfer models (RTMs) help understand the interactions of radiation with vegetation and atmosphere. However, advanced RTMs can be computationally burdensome, which makes them impractical in many real applications, especially when many state conditions and model couplings need to be studied. To overcome this problem, it is proposed to substitute RTMs through surrogate meta-models also named emulators. Emulators approximate the functioning of RTMs through statistical learning regression methods, and can open many new applications because of their computational efficiency and outstanding accuracy. Emulators allow fast global sensitivity analysis (GSA) studies on advanced, computationally expensive RTMs. As a proof-of-concept, three machine learning regression algorithms (MLRAs) were tested to function as emulators for the leaf RTM PROSPECT-4, the canopy RTM PROSAIL, and the computationally expensive atmospheric RTM MODTRAN5. Selected MLRAs were: kernel ridge regression (KRR), neural networks (NN) and Gaussian processes regression (GPR). For each RTM, 500 simulations were generated for training and validation. The majority of MLRAs were excellently validated to function as emulators with relative errors well below 0.2%. The emulators were then put into a GSA scheme and compared against GSA results as generated by original PROSPECT-4 and PROSAIL runs. NN and GPR emulators delivered identical GSA results, while processing speed compared to the original RTMs doubled for PROSPECT-4 and tripled for PROSAIL. Having the emulator-GSA concept successfully tested, for six MODTRAN5 atmospheric transfer functions (outputs), i.e., direct and diffuse at-surface solar irradiance (E di f , E dir ), direct and diffuse upward transmittance (T dir , T di f ), spherical albedo (S) and path radiance (L 0 ), the most accurate MLRA's were subsequently applied as emulator into the GSA scheme. The sensitivity analysis along the 400-2500 nm spectral range took no more than a few minutes on a contemporary computer-in comparison, the same analysis in the original MODTRAN5 would have taken over a month. Key atmospheric drivers were identified, which are on the one hand aerosol optical properties, i.e., aerosol optical thickness (AOT), Angstrom coefficient (AMS) and scattering asymmetry variable (G), mostly driving diffuse atmospheric components, E di f and T di f ; and those affected by atmospheric scattering, L 0 and S. On the other hand, as expected, AOT, AMS and columnar water vapor (CWV) in the absorption regions mostly drive E dir and T dir atmospheric functions. The presented emulation schemes showed very promising results in replacing costly RTMs, and we think they can contribute to the adoption of machine learning techniques in remote sensing and environmental applications.

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Section: Modtran5mentioning

confidence: 99%

Emulation of Leaf, Canopy and Atmosphere Radiative Transfer Models for Fast Global Sensitivity Analysis

et al. 2016

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“…Two candidate algorithms were identified: FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes), and Parallel MODTRAN. The details of the algorithms are not presented here, but are available in [33] and [34].…”

Section: C3 Atmospheric Correctionmentioning

confidence: 99%

Maximum a Posteriori (MAP) Estimates for Hyperspectral Image Enhancement

Wilson¹,

Lindgren²,

Fitzgerald³

et al. 2004

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The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Department of Defense, Washington Headquarters Services, Directorate for Information , 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. REPORT DATE (DD-MM-YY)2. REPORT TYPE 3. DATES COVERED (From -To) SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSORING/MONITORING AGENCY ACRONYM(S)AFRL AFRL-SN-WP-TR-2004-1255 DISTRIBUTION/AVAILABILITY STATEMENTApproved for public release; distribution is unlimited. SUPPLEMENTARY NOTESReport contains color. This is a Small Business Innovation Research (SBIR) Phase II report. ABSTRACTIn completing this Phase 2 SBIR, ATK Mission Research with help from the University of Dayton, has accomplished the intended goal of the SBIR program by developing a commercially marketable Hyperspectral Image Enhancement system. This system implements a coherent and rigorous physics model for HSI spatial resolution enhancement on a Mercury Multi-Computer platform.Our SBIR research effort has led to the completion of a Maximum A Posteriori (MAP) algorithm for constructing a highspatial resolution hyperspectral enhancement from a low-spatial resolution hyperspectral image, by incorporating registered higher-resolution imagery from an auxiliary sensor. Although we have focused on the enhancement of a hyperspectral image using high-resolution panchromatic data, the estimation software delivered allows for any number of spectral bands to be employed from the auxiliary sensor. SUBJECT TERMS Chapter 1 Executive SummaryHyperspectral Imagery (HSI) remains the most advanced tool for passive remote sensing. A hyperspectral data cube can contain a vast range of spatial, spectral, and temporal information. Interest in Hyperspectral imagery is creating markets for such applications as military intelligence, tactical military operations, geology, forestry, cultural studies, and environmental studies. These disparate remote sensing communities require sophisticated hyperspectral techniques to solve intrinsically difficult problems such as automated local area change detection, small target detection in clutter, material identification at the sub-pixel scale, target class discrimination for targeting, and geophysical or biochemical parameter estimation for natural resource development or environmental monitoring.However there are li...

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“…In particular, the development of computationally efficient techniques for transforming the massive amount of hyperspectral data collected on a daily basis into scientific understanding is critical for space-based Earth science and planetary exploration [10][11][12]. The wealth of spatial and spectral information provided by last-generation hyperspectral instruments has opened ground-breaking perspectives in many applications, including environmental modeling and assessment, target detection for military and defense/security purposes, urban planning and management studies, risk/hazard prevention and response including wild land fire tracking, biological threat detection, monitoring of oil spills and other types of chemical contamination [13].…”

Section: Introductionmentioning

confidence: 99%

Improving the Performance of Hyperspectral Image and Signal Processing Algorithms Using Parallel, Distributed and Specialized Hardware-Based Systems

Plaza

Vegas

2010

J Sign Process Syst

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Advances in sensor technology are revolutionizing the way remotely sensed data is collected, managed and analyzed. The incorporation of latestgeneration sensors to airborne and satellite platforms is currently producing a nearly continual stream of highdimensional data, and this explosion in the amount of collected information has rapidly created new processing challenges. For instance, hyperspectral signal processing is a new technique in remote sensing that generates hundreds of spectral bands at different wavelength channels for the same area on the surface of the Earth. Many current and future applications of remote sensing in Earth science, space science, and soon in exploration science will require (near) realtime processing capabilities. In recent years, several efforts have been directed towards the incorporation of high-performance computing (HPC) systems and architectures in remote sensing missions. With the aim of providing an overview of current and new trends in parallel and distributed systems for remote sensing applications, this paper explores three HPC-based paradigms for efficient implementation of the Pixel Purity Several different parallel programming techniques are used to improve the performance of the PPI on a variety of parallel platforms, including a set of message passing interface (MPI)-based implementations on a massively parallel Beowulf cluster at NASA's Goddard Space Flight Center in Maryland and on a variety of heterogeneous networks of workstations at University of Maryland; a Handel-C implementation of the algorithm on a Virtex-II field programmable gate array (FPGA); and a compute unified device architecture (CUDA)-based implementation on graphical processing units (GPUs) of NVidia. Combined, these parts deliver an excellent snapshot of the state-of-the-art in those areas, and offer a thoughtful perspective on the potential and emerging challenges of adapting HPC systems to remote sensing problems.

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MODTRAN on supercomputers and parallel computers

Cited by 33 publications

References 0 publications

Emulation of Leaf, Canopy and Atmosphere Radiative Transfer Models for Fast Global Sensitivity Analysis

Emulation of Leaf, Canopy and Atmosphere Radiative Transfer Models for Fast Global Sensitivity Analysis

Maximum a Posteriori (MAP) Estimates for Hyperspectral Image Enhancement

Improving the Performance of Hyperspectral Image and Signal Processing Algorithms Using Parallel, Distributed and Specialized Hardware-Based Systems

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