Design of an airborne Fourier transform visible hyperspectral imaging system for light aircraft environmental remote sensing

1995 ◽  
Author(s):  
Leonard John Otten III ◽  
Eugene W. Butler ◽  
Bruce Rafert ◽  
R. Glenn Sellar
Keyword(s):  
Remote Sensing ◽  
Fourier Transform ◽  
Hyperspectral Imaging ◽  
Imaging System ◽  
Environmental Remote Sensing

scholarly journals Hyperspectral Imaging System Calibration Using Image Translations and Fourier Transform

2008 ◽  
Vol 16 (4) ◽  
pp. 371-380 ◽  
Author(s):  
Nathalie Gorretta ◽  
Gilles Rabatel ◽  
Jean-Michel Roger ◽  
Christophe Fiorio ◽  
Camille Lelong ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Hyperspectral Imaging ◽  
Imaging System ◽  
System Calibration

Hyperspectral imaging Fourier transform spectrometers for astronomical and remote sensing observations

1994 ◽  
Author(s):  
Bruce Rafert ◽  
R. Glenn Sellar ◽  
Eirik Holbert ◽  
Joel H. Blatt ◽  
David W. Tyler ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Fourier Transform ◽  
Hyperspectral Imaging

scholarly journals POTENTIAL OF AIRBORNE IMAGING SPECTROSCOPY AT CZECHGLOBE

2016 ◽  
Vol XLI-B1 ◽  
pp. 15-17 ◽  
Author(s):  
J. Hanuš ◽  
T. Fabiánek ◽  
L. Fajmon
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Imaging ◽  
Laser Scanning ◽  
Imaging System ◽  
Spectral Response ◽  
Imaging Spectroscopy ◽  
Laser Scanner ◽  
Current Status ◽  
Functional Changes ◽  
Processing Chain

Ecosystems, their services, structures and functions are affected by complex environmental processes, which are both natural and human-induced and globally changing. In order to understand how ecosystems behave in globally changing environment, it is important to monitor the current status of ecosystems and their structural and functional changes in time and space. An essential tool allowing monitoring of ecosystems is remote sensing (RS). Many ecosystems variables are being translated into a spectral response recorded by RS instruments. It is however important to understand the complexity and synergies of the key ecosystem variables influencing the reflected signal. This can be achieved by analysing high resolution RS data from multiple sources acquired simultaneously from the same platform. Such a system has been recently built at CzechGlobe - Global Change Research Institute (The Czech Academy of Sciences). <br><br> CzechGlobe has been significantly extending its research infrastructure in the last years, which allows advanced monitoring of ecosystem changes at hierarchical levels spanning from molecules to entire ecosystems. One of the CzechGlobe components is a laboratory of imaging spectroscopy. The laboratory is now operating a new platform for advanced remote sensing observations called FLIS (Flying Laboratory of Imaging Spectroscopy). FLIS consists of an airborne carrier equipped with passive RS systems. The core instrument of FLIS is a hyperspectral imaging system provided by Itres Ltd. The hyperspectral system consists of three spectroradiometers (CASI 1500, SASI 600 and TASI 600) that cover the reflective spectral range from 380 to 2450 nm, as well as the thermal range from 8 to 11.5 μm. The airborne platform is prepared for mounting of full-waveform laser scanner Riegl-Q780 as well, however a laser scanner is not a permanent part of FLIS. In 2014 the installation of the hyperspectral scanners was completed and the first flights were carried out with all sensors. <br><br> The new hyperspectral imaging system required adaptations in the data pre-processing chain. The established pre-processing chain (radiometric, atmospheric and geometric corrections), which was tailored mainly to the AISA Eagle instrument operated at CzechGlobe since 2004, has been now modified to fit the new system and users needs. Continuous development of the processing chain is now focused mainly on establishing pre-processing of thermal data including emissivity estimation and also on joint processing of hyperspectral and laser scanning data.

scholarly journals POTENTIAL OF AIRBORNE IMAGING SPECTROSCOPY AT CZECHGLOBE

2016 ◽  
Vol XLI-B1 ◽  
pp. 15-17 ◽  
Author(s):  
J. Hanuš ◽  
T. Fabiánek ◽  
L. Fajmon
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Imaging ◽  
Laser Scanning ◽  
Imaging System ◽  
Spectral Response ◽  
Imaging Spectroscopy ◽  
Laser Scanner ◽  
Current Status ◽  
Functional Changes ◽  
Processing Chain

Ecosystems, their services, structures and functions are affected by complex environmental processes, which are both natural and human-induced and globally changing. In order to understand how ecosystems behave in globally changing environment, it is important to monitor the current status of ecosystems and their structural and functional changes in time and space. An essential tool allowing monitoring of ecosystems is remote sensing (RS). Many ecosystems variables are being translated into a spectral response recorded by RS instruments. It is however important to understand the complexity and synergies of the key ecosystem variables influencing the reflected signal. This can be achieved by analysing high resolution RS data from multiple sources acquired simultaneously from the same platform. Such a system has been recently built at CzechGlobe - Global Change Research Institute (The Czech Academy of Sciences). <br><br> CzechGlobe has been significantly extending its research infrastructure in the last years, which allows advanced monitoring of ecosystem changes at hierarchical levels spanning from molecules to entire ecosystems. One of the CzechGlobe components is a laboratory of imaging spectroscopy. The laboratory is now operating a new platform for advanced remote sensing observations called FLIS (Flying Laboratory of Imaging Spectroscopy). FLIS consists of an airborne carrier equipped with passive RS systems. The core instrument of FLIS is a hyperspectral imaging system provided by Itres Ltd. The hyperspectral system consists of three spectroradiometers (CASI 1500, SASI 600 and TASI 600) that cover the reflective spectral range from 380 to 2450 nm, as well as the thermal range from 8 to 11.5 μm. The airborne platform is prepared for mounting of full-waveform laser scanner Riegl-Q780 as well, however a laser scanner is not a permanent part of FLIS. In 2014 the installation of the hyperspectral scanners was completed and the first flights were carried out with all sensors. <br><br> The new hyperspectral imaging system required adaptations in the data pre-processing chain. The established pre-processing chain (radiometric, atmospheric and geometric corrections), which was tailored mainly to the AISA Eagle instrument operated at CzechGlobe since 2004, has been now modified to fit the new system and users needs. Continuous development of the processing chain is now focused mainly on establishing pre-processing of thermal data including emissivity estimation and also on joint processing of hyperspectral and laser scanning data.

High areal rate longwave-infrared hyperspectral imaging for environmental remote sensing

2018 ◽  
Author(s):  
Patrick D. Johnson ◽  
David M. Tratt ◽  
Eric R. Keim ◽  
Jeffrey L. Hall ◽  
Kerry N. Buckland ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Imaging ◽  
Environmental Remote Sensing

scholarly journals Remote Sensing of Explosives-Induced Stress in Plants: Hyperspectral Imaging Analysis for Remote Detection of Unexploded Threats

2019 ◽  
Vol 11 (15) ◽  
pp. 1827 ◽  
Author(s):  
Paul V. Manley ◽  
Vasit Sagan ◽  
Felix B. Fritschi ◽  
Joel G. Burken
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Imaging ◽  
Vegetation Index ◽  
Near Infrared ◽  
Imaging System ◽  
Principal Component ◽  
Field Capacity ◽  
Remotely Sensed ◽  
Control Group ◽  
Red Edge

Explosives contaminate millions of hectares from various sources (partial detonations, improper storage, and release from production and transport) that can be life-threatening, e.g., landmines and unexploded ordnance. Exposure to and uptake of explosives can also negatively impact plant health, and these factors can be can be remotely sensed. Stress induction was remotely sensed via a whole-plant hyperspectral imaging system as two genotypes of Zea mays, a drought-susceptible hybrid and a drought-tolerant hybrid, and a forage Sorghum bicolor were grown in a greenhouse with one control group, one group maintained at 60% soil field capacity, and a third exposed to 250 mg kg−1 Royal Demolition Explosive (RDX). Green-Red Vegetation Index (GRVI), Photochemical Reflectance Index (PRI), Modified Red Edge Simple Ratio (MRESR), and Vogelmann Red Edge Index 1 (VREI1) were reduced due to presence of explosives. Principal component analyses of reflectance indices separated plants exposed to RDX from control and drought plants. Reflectance of Z. mays hybrids was increased from RDX in green and red wavelengths, while reduced in near-infrared wavelengths. Drought Z. mays reflectance was lower in green, red, and NIR regions. S. bicolor grown with RDX reflected more in green, red, and NIR wavelengths. The spectra and their derivatives will be beneficial for developing explosive-specific indices to accurately identify plants in contaminated soil. This study is the first to demonstrate potential to delineate subsurface explosives over large areas using remote sensing of vegetation with aerial-based hyperspectral systems.

scholarly journals Advances in hyperspectral remote sensing I: The visible Fourier transform hyperspectral imager

2015 ◽  
Author(s):  
J. Rafert
Keyword(s):  
Remote Sensing ◽  
Fourier Transform ◽  
Research And Development ◽  
Hyperspectral Imaging ◽  
Hyperspectral Remote Sensing ◽  
Optical Engineering ◽  
Successful Launch ◽  
Modulated Imaging ◽  
Institute Of Technology ◽  
New Applications

We discuss early hyperspectral research and development activities during the 1990s that led to the deployment of aircraft and satellite payloads whose heritage was based on the use of visible, spatially modulated, imaging Fourier transform spectrometers, beginning with early experiments at the Florida Institute of Technology, through successful launch and deployment of the Visible Fourier Transform Hyperspectral Imager on MightySat II.1 on 19 July 2000. In addition to a brief chronological overview, we also discuss several of the most interesting optical engineering challenges that were addressed over this timeframe, present some as-yet un-exploited features of field-widened (slit-less) SMIFTS instruments, and present some images from ground-based, aircraft-based and satellite-based instruments that helped provide the impetus for the proliferation and development of entire new families of instruments and countless new applications for hyperspectral imaging.

scholarly journals Precision Cropping by Remotely Sensed Prorotype Plots and Calibration in the Complex Domain

2002 ◽  
Author(s):  
Gerald L. Anderson ◽  
Kalman Peleg
Keyword(s):  
Remote Sensing ◽  
Plant Growth ◽  
Prediction Model ◽  
Least Squares ◽  
Hyperspectral Imaging ◽  
Imaging System ◽  
Interpolation Method ◽  
Regression Method ◽  
Remote Sensing Imagery ◽  
Leaf Nitrate

This research report describes a methodology whereby multi-spectral and hyperspectral imagery from remote sensing, is used for deriving predicted field maps of selected plant growth attributes which are required for precision cropping. A major task in precision cropping is to establish areas of the field that differ from the rest of the field and share a common characteristic. Yield distribution f maps can be prepared by yield monitors, which are available for some harvester types. Other field attributes of interest in precision cropping, e.g. soil properties, leaf Nitrate, biomass etc. are obtained by manual sampling of the filed in a grid pattern. Maps of various field attributes are then prepared from these samples by the "Inverse Distance" interpolation method or by Kriging. An improved interpolation method was developed which is based on minimizing the overall curvature of the resulting map. Such maps are the ground truth reference, used for training the algorithm that generates the predicted field maps from remote sensing imagery. Both the reference and the predicted maps are stratified into "Prototype Plots", e.g. 15xl5 blocks of 2m pixels whereby the block size is 30x30m. This averaging reduces the datasets to manageable size and significantly improves the typically poor repeatability of remote sensing imaging systems. In the first two years of the project we used the Normalized Difference Vegetation Index (NDVI), for generating predicted yield maps of sugar beets and com. The NDVI was computed from image cubes of three spectral bands, generated by an optically filtered three camera video imaging system. A two dimensional FFT based regression model Y=f(X), was used wherein Y was the reference map and X=NDVI was the predictor. The FFT regression method applies the "Wavelet Based", "Pixel Block" and "Image Rotation" transforms to the reference and remote images, prior to the Fast - Fourier Transform (FFT) Regression method with the "Phase Lock" option. A complex domain based map Yfft is derived by least squares minimization between the amplitude matrices of X and Y, via the 2D FFT. For one time predictions, the phase matrix of Y is combined with the amplitude matrix ofYfft, whereby an improved predicted map Yplock is formed. Usually, the residuals of Y plock versus Y are about half of the values of Yfft versus Y. For long term predictions, the phase matrix of a "field mask" is combined with the amplitude matrices of the reference image Y and the predicted image Yfft. The field mask is a binary image of a pre-selected region of interest in X and Y. The resultant maps Ypref and Ypred aremodified versions of Y and Yfft respectively. The residuals of Ypred versus Ypref are even lower than the residuals of Yplock versus Y. The maps, Ypref and Ypred represent a close consensus of two independent imaging methods which "view" the same target. In the last two years of the project our remote sensing capability was expanded by addition of a CASI II airborne hyperspectral imaging system and an ASD hyperspectral radiometer. Unfortunately, the cross-noice and poor repeatability problem we had in multi-spectral imaging was exasperated in hyperspectral imaging. We have been able to overcome this problem by over-flying each field twice in rapid succession and developing the Repeatability Index (RI). The RI quantifies the repeatability of each spectral band in the hyperspectral image cube. Thereby, it is possible to select the bands of higher repeatability for inclusion in the prediction model while bands of low repeatability are excluded. Further segregation of high and low repeatability bands takes place in the prediction model algorithm, which is based on a combination of a "Genetic Algorithm" and Partial Least Squares", (PLS-GA). In summary, modus operandi was developed, for deriving important plant growth attribute maps (yield, leaf nitrate, biomass and sugar percent in beets), from remote sensing imagery, with sufficient accuracy for precision cropping applications. This achievement is remarkable, given the inherently high cross-noice between the reference and remote imagery as well as the highly non-repeatable nature of remote sensing systems. The above methodologies may be readily adopted by commercial companies, which specialize in proving remotely sensed data to farmers.

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