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Society-for Experimental-Mechanics (SEM) Annual Conference and Exposition on Experimental and Applied Mechanics

作者： Holland, Stephen D. Iowa State Univ Ames IA 50011 USA

ISBN: (纸本)9783319950747;9783319950730

We present a model-based inversion approach to the interpretation of flash thermography data. Traditionally, flash thermography has been image sequences have been hard to interpret in the presence of lateral heat flows. This is a particularly significant problem for composite laminate materials where heat conducts readily in the fiber planes. We use a linear inversion approach to represent the observed surface temperature as a superposition of contributions from buried reflectors. We investigate the additional complexities of considering cases where the specimen is curved, changing the nature of the heat flow. By solving a large linear inversion problem we are able to determine the strengths of the buried reflectors and create a concrete representation of observed defects. Results from this approach are compared to more traditional analysis approaches for flash thermography.

关键词： Flash thermography Nondestructive evaluation (NDE) inversion algorithms Model-based inversion

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Absorption spectrum of phytoplankton pigments derived from hyperspectral remote-sensing reflectance

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REMOTE SENSING OF ENVIRONMENT 2004年第3期89卷 361-368页

作者： Lee, ZP Carder, KL USN Res Lab Stennis Space Ctr MS 39529 USA Univ S Florida Coll Marine Sci St Petersburg FL 33701 USA

For a data set collected around Baja California with chlorophyll-a concentration ((chl-a)) ranging from 0.16 to 11.3 mg/m(3), hyperspectral absorption spectra of phytoplankton pigments were independently inverted from hyperspectral remote-sensing reflectance using a newly developed ocean-color algorithm. The derived spectra were then compared with those measured from water samples using the filter-pad technique, and an average difference of 21.4% was obtained. These results demonstrate that the inversion algorithm worked quite well for the coastal waters observed and suggest a potential of using hyperspectral remote sensing to retrieve both chlorophyll-a and other accessory pigments. (C) 2003 Elsevier Inc. All rights reserved.

关键词： absorption spectrum of phytoplankton pigments inversion algorithms

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Soil moisture mapping using Sentinel-1 images: Algorithm and preliminary validation

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REMOTE SENSING OF ENVIRONMENT 2013年 134卷 234-248页

作者： Paloscia, S. Pettinato, S. Santi, E. Notarnicola, C. Pasolli, L. Reppucci, A. Natl Res Council IFAC CNR Inst Appl Phys I-50019 Florence Italy EURAC Res Bolzano Italy Starlab Barcelona Spain

The main objective of this research is to develop, test and validate a soil moisture content (SMC) algorithm for GMES Sentinel-1 characteristics. The SMC product, which is to be generated from Sentinel-1 data, requires an algorithm capable of processing operationally in near-real-time and delivering the product to the GMES services within 3 h from observation. An approach based on an Artificial Neural Network (ANN) has been proposed that represents a good compromise between retrieval accuracy and processing time, thus enabling compliance with the timeliness requirements. The algorithm has been tested and subsequently validated in several test areas in Italy, Australia, and Spain. In all cases the validation results were very much in line with GMES requirements (with RMSE generally <4%SMC - between 1.67%SMC and 6.68%SMC - and very low bias), except for the case of the test area in Spain, where the validation results were penalized by the availability of only VV polarized SAR images and MODIS low-resolution NDVI. Nonetheless, the obtained RMSE was slightly higher than 4%SMC. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Sentinel-1 Backscattering coefficient Soil moisture maps inversion algorithms Artificial Neural Network

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The potential of multifrequency SAR images for estimating forest biomass in Mediterranean areas

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REMOTE SENSING OF ENVIRONMENT 2017年 200卷 63-73页

作者： Santi, Emanuele Paloscia, Simonetta Pettinato, Simone Fontanelli, Giacomo Mura, Matteo Zolli, Catherine Maselli, Fabio Chiesi, Marta Bottai, Lorenzo Chirici, Gherardo Natl Res Council Italy IFAC CNR Inst Appl Phys Via Madonna Piano 10 I-50019 Florence Italy Univ Firenze Dept Agr Food & Forestry Syst Via San Bonaventura 13 I-50145 Florence Italy Italian Acad Forest Sci Pzza Edison 11 I-50133 Florence Italy Natl Res Council Italy IBIMET CNR Inst Biometeorol Via Madonna Piano 10 I-50019 Florence Italy LaMMA Consortium Via Madonna Piano 10 I-50019 Florence Italy

The extraction of forest information from SAR images is particularly complex in Mediterranean areas, since they are characterized by high spatial fragmentation and heterogeneity. We have investigated the use of multi-frequency SAR data from different sensors (ALOS/PALSAR and ENVISAT/ASAR) for estimating forest biomass in two test areas in Central Italy (San Rossore and Molise), where detailed in-situ measurements and Airborne Laser Scanning (ALS) data were available. The study focused on the estimation of growing stock volume (GS, in m(3)/ha) by using an inversion algorithm based on artificial neural networks (ANN). The ANN algorithm was first appropriately trained using the available GS estimates obtained from ALS data. The potential of this algorithm was then improved through the innovative use of a simulated dataset, generated by a forward electromagnetic model based on the Radiative Transfer Theory (RTT). The algorithm is able to merge SAR data at L and C bands for predicting GS in diversified Mediterranean environments. The performed analyses indicated that GS was correctly estimated by integrating information from L and C bands on both test areas, with the following statistics: R > 0.97 and RMSE = 28.5 m(3)/ha for the independent test, and R = 0.86 and RMSE approximate to 77 m(3)/ha for the final independent validation, the latter performed on the forest stands of both areas not included in the ALS acquisitions and where conventional measurements were available. The research then illustrates the potential of using the obtained GS estimates from SAR data to drive the simulations of forest net primary production (NPP). This experiment produced spatially explicit estimates of GS current annual increments that are slightly less accurate than those obtained from ground observations (R = 0.75 and RMSE approximate to 1.5 m(3)/ha/year).

关键词： SAR Growing stock volume ANN inversion algorithms Net primary production (NPP)

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Estimation of material parameters from slow and fast shear waves in an incompressible, transversely isotropic material

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JOURNAL OF BIOMECHANICS 2015年第15期48卷 4002-4009页

作者： Tweten, Dennis J. Okamoto, Ruth J. Schmidt, John L. Garbow, Joel R. Bayly, Philip V. Washington Univ Dept Mech Engn & Mat Sci St Louis MO 63130 USA Washington Univ Dept Radiol St Louis MO 63130 USA Washington Univ Dept Biomed Engn St Louis MO 63130 USA

This paper describes a method to estimate mechanical properties of soft, anisotropic materials from measurements of shear waves with specific polarization and propagation directions. This method is applicable to data from magnetic resonance elastography (MRE), which is a method for measuring shear waves in live subjects or in vitro samples. Here, we simulate MRE data using finite element analysis. A nearly incompressible, transversely isotropic (Ill) material model with three parameters (shear modulus, shear anisotropy, and tensile anisotropy) is used, which is appropriate for many fibrous, biological tissues. Both slow and fast shear waves travel concurrently through such a material with speeds that depend on the propagation direction relative to fiber orientation. A three-parameter estimation approach based on directional filtering and isolation of slow and fast shear wave components (directional filter inversion, or DFI) is introduced. Wave speeds of each isolated shear wave component are estimated using local frequency estimation (LFE), and material properties are calculated using weighted least squares. Data from multiple finite element simulations are used to assess the accuracy and reliability of DFI for estimation of anisotropic material parameters. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： MR elastography Shear waves Anisotropy Transversely isotropic material inversion algorithms

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Unique temperature profiles for the atmosphere below an observer from sunset images

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APPLIED OPTICS 1997年第27期36卷 6957-6961页

作者： Bruton, WD Kattawar, GW TEXAS A&M UNIV DEPT PHYSCTR THEORET PHYSCOLLEGE STNTX 77843

We were able to transform the usual refraction equations for an observer within a spherically symmetric atmosphere into an Abel integral equation that has a unique inverse. It is now possible to extract accurate temperature profiles of the atmosphere below an observer over the marine boundary layer with use of simple edge-finding software on solar images at sunset. Several examples are presented demonstrating the efficiency of the method. (C) 1997 Optical Society of America.

关键词： refraction temperature profiles inversion algorithms ocean optics

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Thermal response tests for the identification of soil thermal parameters: A review

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RENEWABLE ENERGY 2021年 173卷 1123-1135页

作者： Zhang, Xueping Han, Zongwei Ji, Qiang Zhang, Hongzhi Li, Xiuming Northeastern Univ Sch Met SEP Key Lab Ecoind Shenyang Peoples R China

Ground source heat pump systems (GSHPs) for soil heat utilization have been widely concerned in the world due to their energy saving, high efficiency, and no pollutant emission. The thermal parameters of the local soil must be known first when designing GSHPs, and the in-situ thermal response test (TRT) is currently the conventional method for determining their values. Given this, this paper deals with the related contents of parameter identification based on TRT from four aspects. First, various modeling methods for ground heat exchanger (GHE) and inversion algorithms are introduced and compared. Next, many factors that influence the identification accuracy are summarized, including test conditions, external disturbance, groundwater seepage and subsurface natural convection. Then, the negative in-fluence of correlation between parameters on identification results is analyzed, and the research progress of the uncertainty assessment of identification results is introduced, which can improve the reliability of the results. Furthermore, the characteristics of various advanced TRT setups are also summarized. Finally, several imperfects and objectives for improving the reliability of parameters identification are discussed. This work provides useful information for improving the identification accuracy of soil thermal pa-rameters so that the GHSPs can be designed more reliably. (c) 2020 Elsevier Ltd. All rights reserved.

关键词： Ground source heat pump Thermal response test Ground heat exchanger inversion algorithms Uncertainty assessment

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Comparison between SAR Soil Moisture Estimates and Hydrological Model Simulations over the Scrivia Test Site

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REMOTE SENSING 2013年第10期5卷 4961-4976页

作者： Santi, Emanuele Paloscia, Simonetta Pettinato, Simone Notarnicola, Claudia Pasolli, Luca Pistocchi, Alberto Natl Res Council IFAC CNR Ist Fis Applicata Nello Carrara I-50019 Florence Italy EURAC Res Inst Appl Remote Sensing I-39100 Bolzano Italy Gecosistema R&D Unit Trentino Suedtirol I-39100 Bolzano Italy

In this paper, the results of a comparison between the soil moisture content (SMC) estimated from C-band SAR, the SMC simulated by a hydrological model, and the SMC measured on ground are presented. The study was carried out in an agricultural test site located in North-west Italy, in the Scrivia river basin. The hydrological model used for the simulations consists of a one-layer soil water balance model, which was found to be able to partially reproduce the soil moisture variability, retaining at the same time simplicity and effectiveness in describing the topsoil. SMC estimates were derived from the application of a retrieval algorithm, based on an Artificial Neural Network approach, to a time series of ENVISAT/ASAR images acquired over the Scrivia test site. The core of the algorithm was represented by a set of ANNs able to deal with the different SAR configurations in terms of polarizations and available ancillary data. In case of crop covered soils, the effect of vegetation was accounted for using NDVI information, or, if available, for the cross-polarized channel. The algorithm results showed some ability in retrieving SMC with RMSE generally <0.04 m(3)/m(3) and very low bias (i.e., <0.01 m(3)/m(3)), except for the case of VV polarized SAR images: in this case, the obtained RMSE was somewhat higher than 0.04 m(3)/m(3) (0.058 m(3)/m(3)). The algorithm was implemented within the framework of an ESA project concerning the development of an operative algorithm for the SMC retrieval from Sentinel-1 data. The algorithm should take into account the GMES requirements of SMC accuracy (5% in volume), spatial resolution (1 km) and timeliness (3 h from observation). The SMC estimated by the SAR algorithm, the SMC estimated by the hydrological model, and the SMC measured on ground were found to be in good agreement. The hydrological model simulations were performed at two soil depths: 30 and 5 cm and showed that the 30 cm simulations indicated, as expected, SMC values high

关键词： SAR data soil moisture hydrological model Artificial Neural Networks inversion algorithms

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Optoacoustic model-based inversion using anisotropic adaptive total-variation regularization

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PHOTOACOUSTICS 2019年 16卷 100142-100142页

作者： Biton, Shai Arbel, Nadav Drozdov, Gilad Gilboa, Guy Rosenthal, Amir Technion Israel Inst Technol Andrew & Erna Viterbi Fac Elect Engn IL-31999 Haifa Israel

In optoacoustic tomography, image reconstruction is often performed with incomplete or noisy data, leading to reconstruction errors. Significant improvement in reconstruction accuracy may be achieved in such cases by using nonlinear regularization schemes, such as total-variation minimization and L-1-based sparsity-preserving schemes. In this paper, we introduce a new framework for optoacoustic image reconstruction based on adaptive anisotropic total-variation regularization, which is more capable of preserving complex boundaries than conventional total-variation regularization. The new scheme is demonstrated in numerical simulations on blood-vessel images as well as on experimental data and is shown to be more capable than the total-variation-L-1 scheme in enhancing image contrast.

关键词： Optoacoustic imaging Total variation inversion algorithms Model-based reconstruction

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Review of Satellite Remote Sensing of Carbon Dioxide inversion and Assimilation

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REMOTE SENSING 2024年第18期16卷 3394页

作者： Hu, Kai Feng, Xinyan Zhang, Qi Shao, Pengfei Liu, Ziran Xu, Yao Wang, Shiqian Wang, Yuanyuan Wang, Han Di, Li Xia, Min Nanjing Univ Informat Sci & Technol NUIST Sch Automat Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Jiangsu Collaborat Innovat Ctr Atmospher Environm Nanjing 210044 Peoples R China Univ Reading Sch Math Phys & Computat Sci POB 217 Reading RG6 6AH Berks England State Grid Henan Elect Power Co Econ & Tech Res Inst Zhengzhou 450052 Peoples R China State Grid Henan Elect Power Co Zhengzhou 450003 Peoples R China

With the rapid development of satellite remote sensing technology, carbon-cycle research, as a key focus of global climate change, has also been widely developed in terms of carbon source/sink-research methods. The internationally recognized "top-down" approach, which is based on satellite observations, is an important means to verify greenhouse gas-emission inventories. This article reviews the principles, categories, and development of satellite detection payloads for greenhouse gases and introduces inversion algorithms and datasets for satellite remote sensing of XCO2. It emphasizes inversion methods based on machine learning and assimilation algorithms. Additionally, it presents the technology and achievements of carbon-assimilation systems used to estimate carbon fluxes. Finally, the article summarizes and prospects the future development of carbon-assimilation inversion to improve the accuracy of estimating and monitoring Earth's carbon-cycle processes.

关键词： satellite remote sensing inversion algorithms XCO2 carbon assimilation

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