Multifrequency 3-D Inversion of GREATEM Data by BCGS-FFT-BIM

Chen, Qiu; Liang, Bingyang; Han, Feng; Liu, Hai; Zhu, Chunhui; Liu, Na; Liu, Fubo; Fang, Guangyou; Liu, Qing

doi:10.1109/tgrs.2018.2873363

Cited by 15 publications

(9 citation statements)

References 35 publications

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“…Imaging the interior of otherwise inaccessible objects with electromagnetic waves is a powerful ability. Focusing on non-linear inversion, where quantitative images of the material parameters are sought, example applications include finding and monitoring oil and gas [1], [2], detecting underground mineral deposits [3], detecting and helping treat cancer and strokes [4]- [7], determining the contents of hidden industrial processes [8], and (considered herein) preventing the loss of crops after harvest [9]- [12].…”

Section: Introductionmentioning

confidence: 99%

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Phaseless Parametric Inversion for System Calibration and Obtaining Prior Information

et al. 2019

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Electromagnetic inversion systems require that the experimental data be calibrated to the computational inversion model being used. In addition, accurate prior information provided to the inversion algorithm leads to higher-quality images. For some applications of inversion, such as stored grain imaging or geophysical inversion, known (calibration) targets cannot be easily introduced into the imaging region and the ability to determine prior information can be limited. In an attempt to solve the problem of calibrating data from such field-inversion systems, we introduce a work flow where: (1) a simple parametric physical model of the scattering background is obtained via a phaseless (magnitude only data) inversion algorithm that works on phase-corrupted, uncalibrated total-field measurements, and (2) we then use this simple physical model to generate calibration and prior information for subsequent full-data (magnitude and phase) inversion. Using an example of in-bin stored grain imaging, the inverted parameters are the grain angle of repose, grain height, and the average bulk permittivity of the grain. Using uncalibrated total-field data, we show that the proposed work flow obtains the overall structure of the grain in a bin despite the use of this raw data. We then show that the simple physical model can be used as both a calibration data set as well as the prior information about the grain target in a full-data (magnitude and phase) inversion. The use of this phaseless algorithm means we are able to remotely calibrate imaging systems, and obtain critical prior information about the imaging region without introducing a calibration target or physically measuring the imaging region in other ways.INDEX TERMS Inverse problems, calibration, microwave tomography, inverse imaging.

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Section: Introductionmentioning

confidence: 99%

“…That prior information helps is well known in geophysical inversion, where supplying an accurate multi-layered media model greatly enhances the inversion e.g. [3], [25]. In our specific example considered herein of stored grain imaging, effective imaging (e.g.…”

Section: Introductionmentioning

confidence: 99%

Phaseless Parametric Inversion for System Calibration and Obtaining Prior Information

et al. 2019

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“…Inside the model, the reflectance is calculated using the specific absorption coefficient, K, of each component, which depends on the wavelength [31]. The model has been widely applied in numerous studies [6,[30][31][32][33][34].…”

Section: Prospect-4 Modificationmentioning

confidence: 99%

“…To address this problem, we have generated a rather comprehensive dataset for calibrating PLSR models, based on so-called hybrid methods, combining physically-based models to develop statistical models [6, [30][31][32]. The physical law-based radiative transfer models are not only available to generate datasets containing numerous samples [6, [30][31][32][33][34], but are also important tools for revealing the underlying relationships between vegetation biochemical parameters and reflectance in a more systematic way [35].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Informative Bands Used in Different PLS Regressions for Estimating Leaf Biochemical Contents from Hyperspectral Reflectance

Jin

Wang

2019

Remote Sensing

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Partial least squares (PLS) regression models are widely applied in spectroscopy to estimate biochemical components through hyperspectral reflected information. To build PLS regression models based on informative spectral bands, rather than strongly collinear bands contained in the full spectrum, is essential for upholding the performance of models. Yet no consensus has ever been reached on how to select informative bands, even though many techniques have been proposed for estimating plant properties using the vast array of hyperspectral reflectance. In this study, we designed a series of virtual experiments by introducing a dummy variable (Cd) with convertible specific absorption coefficients (SAC) into the well-accepted leaf reflectance PROSPECT-4 model for evaluating popularly adopted informative bands selection techniques, including stepwise-PLS, genetic algorithms PLS (GA-PLS) and PLS with uninformative variable elimination (UVE-PLS). Such virtual experiments have clearly defined responsible wavelength regions related to the dummy input variable, providing objective criteria for model evaluation. Results indicated that although all three techniques examined may estimate leaf biochemical contents efficiently, in most cases the selected bands, unfortunately, did not exactly match known absorption features, casting doubts on their general applicability. The GA-PLS approach was comparatively more efficient at accurately locating the informative bands (with physical and biochemical mechanisms) for estimating leaf biochemical properties and is, therefore, recommended for further applications. Through this study, we have provided objective evaluations of the potential of PLS regressions, which should help to understand the pros and cons of PLS regression models for estimating vegetation biochemical parameters.

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“…Imaging objects embedded in inhomogeneous media is of great importance to both civilian and military applications, such as biomedical imaging [1, 2], geophysics [3] and demining [4], etc. Unfortunately, there are two main challenges preventing us from the accurate and effective reconstruction of objects in inhomogeneous media.…”

Section: Introductionmentioning

confidence: 99%

Accurate and robust inversion of buried objects based on the linear sampling method and differential evolution

Fan

Yin

Gan

2020

IET Radar, Sonar & Navigation

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Multifrequency 3-D Inversion of GREATEM Data by BCGS-FFT-BIM

Cited by 15 publications

References 35 publications

Phaseless Parametric Inversion for System Calibration and Obtaining Prior Information

Phaseless Parametric Inversion for System Calibration and Obtaining Prior Information

Evaluation of Informative Bands Used in Different PLS Regressions for Estimating Leaf Biochemical Contents from Hyperspectral Reflectance

Accurate and robust inversion of buried objects based on the linear sampling method and differential evolution

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