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Languages : en
Pages : 5
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Book Description
In December 1997, the US Department of Energy (DOE) established a Center of Excellence (Hyperspectral-Multispectral Algorithm Research Center, HyMARC) for promoting the research and development of algorithms to exploit spectral imagery. This center is located at the DOE Remote Sensing Laboratory in Las Vegas, Nevada, and is operated for the DOE by Bechtel Nevada. This paper presents the results to date of a research project begun at the center during 1998 to investigate the correction of hyperspectral data for atmospheric aerosols. Results of a project conducted by the Rochester Institute of Technology to define, implement, and test procedures for absolute calibration and correction of hyperspectral data to absolute units of high spectral resolution imagery will be presented. Hybrid techniques for atmospheric correction using image or spectral scene data coupled through radiative propagation models will be specifically addressed. Results of this effort to analyze HYDICE sensor data will be included. Preliminary results based on studying the performance of standard routines, such as Atmospheric Pre-corrected Differential Absorption and Nonlinear Least Squares Spectral Fit, in retrieving reflectance spectra show overall reflectance retrieval errors of approximately one to two reflectance units in the 0.4- to 2.5-micron-wavelength region (outside of the absorption features). These results are based on HYDICE sensor data collected from the Southern Great Plains Atmospheric Radiation Measurement site during overflights conducted in July of 1997. Results of an upgrade made in the model-based atmospheric correction techniques, which take advantage of updates made to the moderate resolution atmospheric transmittance model (MODTRAN 4.0) software, will also be presented. Data will be shown to demonstrate how the reflectance retrieval in the shorter wavelengths of the blue-green region will be improved because of enhanced modeling of multiple scattering effects.
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Languages : en
Pages : 5
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Book Description
In December 1997, the US Department of Energy (DOE) established a Center of Excellence (Hyperspectral-Multispectral Algorithm Research Center, HyMARC) for promoting the research and development of algorithms to exploit spectral imagery. This center is located at the DOE Remote Sensing Laboratory in Las Vegas, Nevada, and is operated for the DOE by Bechtel Nevada. This paper presents the results to date of a research project begun at the center during 1998 to investigate the correction of hyperspectral data for atmospheric aerosols. Results of a project conducted by the Rochester Institute of Technology to define, implement, and test procedures for absolute calibration and correction of hyperspectral data to absolute units of high spectral resolution imagery will be presented. Hybrid techniques for atmospheric correction using image or spectral scene data coupled through radiative propagation models will be specifically addressed. Results of this effort to analyze HYDICE sensor data will be included. Preliminary results based on studying the performance of standard routines, such as Atmospheric Pre-corrected Differential Absorption and Nonlinear Least Squares Spectral Fit, in retrieving reflectance spectra show overall reflectance retrieval errors of approximately one to two reflectance units in the 0.4- to 2.5-micron-wavelength region (outside of the absorption features). These results are based on HYDICE sensor data collected from the Southern Great Plains Atmospheric Radiation Measurement site during overflights conducted in July of 1997. Results of an upgrade made in the model-based atmospheric correction techniques, which take advantage of updates made to the moderate resolution atmospheric transmittance model (MODTRAN 4.0) software, will also be presented. Data will be shown to demonstrate how the reflectance retrieval in the shorter wavelengths of the blue-green region will be improved because of enhanced modeling of multiple scattering effects.
Author: Lee C. Sanders
Publisher:
ISBN:
Category : Remote sensing
Languages : en
Pages : 370
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Book Description
Radiometrically calibrated hyperspectral imagery contains information relating to the material properties of a surface target and the atmospheric layers between the surface target and the sensor. These layers contain gases, aerosol particles, and water vapor, and information about these elements can be extracted from hyperspectral imagery by using specially designed algorithms. This research describes a total sensor radiance-to-ground reflectance inversion program to correct the problem brought about by the presence of these elements.
Author:
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Category : Computer algorithms
Languages : en
Pages : 224
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Author: Andrew Thomas Young
Publisher:
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Category :
Languages : en
Pages : 0
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The work presented in this thesis focuses on improving the anomaly detection process for remotely sensed hyperspectral imagery. This process is split up into three main sections; data reduction, atmospheric correction and anomaly detection. The final stage of this anomaly detection process is the actual anomaly detection algorithm. The initial contribution looks at developing a new type of anomaly detection algorithm based on the Percentage Occupancy Hit-or-Miss Transform. Also, a process for trying to improve the existing Mahalanobis Distance technique for hyperspectral data is explained. Both techniques are then tested on two aerial hyperspectral images, and the results are compared with an established technique the Sequential Maximum Angle Convex Cone algorithm. One of the preprocessing steps of the anomaly detection process is the atmospheric correction phase. In this thesis an interface is developed in MATLAB for the atmospheric modelling software MODTRAN, this interface is then used to find the key parameters that have the most effect on the atmospheric models produced. Having determined the key parameters of a MODTRAN atmospheric model, the models are then used to atmospherically correct eight hyperspectral images; four visible to near-infrared and four short wave infrared hyperspectral images. Two scene based approaches for atmospheric correction are also proposed that use known spectra extracted from the scene to produce an atmospheric transform. All three techniques are then evaluated against existing scene-based approaches, namely Internal Average Relative Reflectance and Dark Object Subtraction. The final contribution focuses on the data reduction phase, images of a wind turbine blade with simulated erosion were taken using a near-infrared hyperspectral camera. By analysing the images produced it was possible to determine the optimal bands necessary to detect each type of erosion. The greyscale images produced for the optimal bands were then compared with standard RGB camera imaging to determine if any more detail was shown in the hyperspectral images. Also by imaging the blade at varying light levels, it was possible to determine when this technique breaks down, however by performing some post-processing of the new data using a polynomial surface subtraction to flatten the images it was again possible to extract additional information from the hyperspectral images.
Author: Shunlin Liang
Publisher: Academic Press
ISBN: 0123859557
Category : Science
Languages : en
Pages : 821
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Book Description
Advanced Remote Sensing is an application-based reference that provides a single source of mathematical concepts necessary for remote sensing data gathering and assimilation. It presents state-of-the-art techniques for estimating land surface variables from a variety of data types, including optical sensors such as RADAR and LIDAR. Scientists in a number of different fields including geography, geology, atmospheric science, environmental science, planetary science and ecology will have access to critically-important data extraction techniques and their virtually unlimited applications. While rigorous enough for the most experienced of scientists, the techniques are well designed and integrated, making the book’s content intuitive, clearly presented, and practical in its implementation. Comprehensive overview of various practical methods and algorithms Detailed description of the principles and procedures of the state-of-the-art algorithms Real-world case studies open several chapters More than 500 full-color figures and tables Edited by top remote sensing experts with contributions from authors across the geosciences
Author:
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Category : Computer algorithms
Languages : en
Pages : 622
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Author:
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ISBN:
Category : Computer algorithms
Languages : en
Pages : 804
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Book Description
Author: Ruiliang Pu
Publisher: CRC Press
ISBN: 1351646931
Category : Science
Languages : en
Pages : 822
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Book Description
Advanced imaging spectral technology and hyperspectral analysis techniques for multiple applications are the key features of the book. This book will present in one volume complete solutions from concepts, fundamentals, and methods of acquisition of hyperspectral data to analyses and applications of the data in a very coherent manner. It will help readers to fully understand basic theories of HRS, how to utilize various field spectrometers and bioinstruments, the importance of radiometric correction and atmospheric correction, the use of analysis, tools and software, and determine what to do with HRS technology and data.
Author: Michael Vohland
Publisher: MDPI
ISBN: 3036508783
Category : Science
Languages : en
Pages : 218
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Book Description
The aim of the Special Issue “Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences” was to present a selection of innovative studies using hyperspectral imaging (HSI) in different thematic fields. This intention reflects the technical developments in the last three decades, which have brought the capacity of HSI to provide spectrally, spatially and temporally detailed data, favoured by e.g., hyperspectral snapshot technologies, miniaturized hyperspectral sensors and hyperspectral microscopy imaging. The present book comprises a suite of papers in various fields of environmental sciences—geology/mineral exploration, digital soil mapping, mapping and characterization of vegetation, and sensing of water bodies (including under-ice and underwater applications). In addition, there are two rather methodically/technically-oriented contributions dealing with the optimized processing of UAV data and on the design and test of a multi-channel optical receiver for ground-based applications. All in all, this compilation documents that HSI is a multi-faceted research topic and will remain so in the future.
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Category :
Languages : en
Pages : 5
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We describe a new VNIR-SWIR atmospheric correction method for multi- and hyperspectral imagery, dubbed QUAC (QUick Atmospheric Correction) that also enables retrieval of the wavelength-dependent optical depth of the aerosol or haze and molecular absorbers. It determines the atmospheric compensation parameters directly from the information contained within the scene using the observed pixel spectra. The approach is based on the empirical finding that the spectral standard deviation of a collection of diverse material spectra, such as the endmember spectra in a scene, is essentially spectrally flat. It allows the retrieval of reasonably accurate reflectance spectra even when the sensor does not have a proper radiometric or wavelength calibration, or when the solar illumination intensity is unknown. The computational speed of the atmospheric correction method is significantly faster than for the first-principles methods, making it potentially suitable for realtime applications. The aerosol optical depth retrieval method, unlike most prior methods, does not require the presence of dark pixels. In this paper, QUAC is applied to atmospherically correction several AVIRIS data sets. Comparisons to the physics-based FLAASH code are also presented.
