Microwave radiometric instrumentation for remote sensing applications
Terrestrial surface radiometric imaging by sensing thermal radiation at centimeter wavelengths to reduce atmospheric scattering attenuation
Pascalar, H. G.↗
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The least-squares mixing models to generate fraction images derived from remote sensing multispectral data
Constrained-least-squares and weighted-least-squares mixing models for generating fraction images derived from remote sensing multispectral data are presented. An experiment considering three components within the pixels-eucalyptus, soil (understory), and shade-was performed. The generated fraction images for shade (shade image) derived from these two methods were compared by considering the performance and computer time. The derived shade images are related to the observed variation in forest structure, i.e., the fraction of inferred shade in the pixel is related to different eucalyptus ages.
Shimabukuro, Yosio Edemir↗
Remote sensing programs and courses in engineering and water resources
The content of typical basic and advanced remote sensing and image interpretation courses are described and typical remote sensing graduate programs of study in civil engineering and in interdisciplinary environmental remote sensing and water resources management programs are outlined. Ideally, graduate programs with an emphasis on remote sensing and image interpretation should be built around a core of five courses: (1) a basic course in fundamentals of remote sensing upon which the more specialized advanced remote sensing courses can build; (2) a course dealing with visual image interpretation; (3) a course dealing with quantitative (computer-based) image interpretation; (4) a basic photogrammetry course; and (5) a basic surveying course. These five courses comprise up to one-half of the course work required for the M.S. degree. The nature of other course work and thesis requirements vary greatly, depending on the department in which the degree is being awarded.
Kiefer, R. W.↗
Developing an undergraduate geography course on digital image processing of remotely sensed data
Problems relating to the development of a digital image processing course in an undergraduate geography environment is discussed. Computer resource requirements, course prerequisites, and the size of the study area are addressed.
Baumann, P. R.↗
Automated Verification of Spatial Resolution in Remotely Sensed Imagery
Image spatial resolution characteristics can vary widely among sources. In the case of aerial-based imaging systems, the image spatial resolution characteristics can even vary between acquisitions. In these systems, aircraft altitude, speed, and sensor look angle all affect image spatial resolution. Image spatial resolution needs to be verified with estimators that include the ground sample distance (GSD), the modulation transfer function (MTF), and the relative edge response (RER), all of which are key components of image quality, along with signal-to-noise ratio (SNR) and dynamic range. Knowledge of spatial resolution parameters is important to determine if features of interest are distinguishable in imagery or associated products, and to develop image restoration algorithms. An automated Spatial Resolution Verification Tool (SRVT) was developed to rapidly determine the spatial resolution characteristics of remotely sensed aerial and satellite imagery. Most current methods for assessing spatial resolution characteristics of imagery rely on pre-deployed engineered targets and are performed only at selected times within preselected scenes. The SRVT addresses these insufficiencies by finding uniform, high-contrast edges from urban scenes and then using these edges to determine standard estimators of spatial resolution, such as the MTF and the RER. The SRVT was developed using the MATLAB programming language and environment. This automated software algorithm assesses every image in an acquired data set, using edges found within each image, and in many cases eliminating the need for dedicated edge targets. The SRVT automatically identifies high-contrast, uniform edges and calculates the MTF and RER of each image, and when possible, within sections of an image, so that the variation of spatial resolution characteristics across the image can be analyzed. The automated algorithm is capable of quickly verifying the spatial resolution quality of all images within a data set, enabling the appropriate use of those images in a number of applications.
Davis, Bruce↗
Applicability of Spectral Decomposition by Varimax-Rotated, Principal Component Analysis to the Surface Biology and Geology (SBG) VNIR Mission Concept
Cyanobacterial and Harmful Algal Blooms (CyanoHABs) are a growing concern in coastal and inland waters. But, spectral interference from multiple constituents in optically complex waters can hamper application of remote sensing using traditional image processing methods. The Kent State University (KSU) spectral decomposition method can be applied to multispectral and hyperspectral remote sensing images (e.g. HICO and the NASA Glenn HSI2) to partition and identify signals related to cyanobacteria, algae, pigment degradation products and suspended sediment in each pixel. Fundamental to the use of remote sensing data is the ability to extract independent signals from correlated hyperspectral VNIR data cubes. The Kent State University varimax-rotated, principal component analysis method (VPCA) is important to integrate into the SBG VNIR mission concept because it provides greater specificity, a software-based SNR boost relative to hardware performance, and can assist with Cal/Val, Modeling and Applications. We present examples of the hyperspectral application of the KSU VPCA method with relevance to SBG. The information extracted by VPCA can be validated spectrally or spatially with laboratory and/or in situ sensors, which capture spatial or time series of information at discrete points within remote sensing images. Comparisons show hyperspectral sensors extract more components than multispectral ones, but more independent information can be extracted from multispectral sensors by VPCA than traditional band ratio approaches. The spectral decomposition method is capable of enhancing the signal to noise ratio (SNR) of the NASA Glenn, second-generation hyperspectral imager by a factor of 7x to 20x, with a spectral reproducibility of ±3%. The spectral decomposition method, when compared against existing remote sensing monitoring methods exhibits both greater specificity and a lower detection limit. The method has been validated with multispectral images in Lake Erie to quantify the Microcystis CyanoHAB and from the Indian River Lagoon, Florida to quantify the Brown Tide resulting from A. lagunesnsis. Field operations in the Western Basin of Lake Erie were conducted using a bbe Fluoroprobe to collect vertical profiles and horizontal tows along a transect from the Toledo to the Detroit Lighthouse during coincident satellite overpasses. Extraction of pixel values from the MODIS Aqua sensor yields agreement between in situ field and lab-based measures of cyanobacterial, cryptophyte, diatoms and green algae, suspended sediment and pigment degradation products with R2>0.8.
hyperspectral↗
Remote sensing
Various imaging techniques are outlined for use in mapping, land use, and land management in Mexico. Among the techniques discussed are pattern recognition and photographic processing. The utilization of information from remote sensing devices on satellites are studied. Multispectral band scanners are examined and software, hardware, and other program requirements are surveyed.
Jinich, A.↗
A Compact, Low-Distortion Imaging Spectrometer for Remote Sensing Applications
Explore the source record for details and available documents.
imaging spectrometer optical design diffractive el↗
Digital image data sets for remote sensing instruction
Several sets of LANDSAT image segments and a set of simple image processing programs operating in batch mode are being used to teach an introductory course in digital image analysis and classification. The image data sets contain only a small number of pixels and can be run quickly on most university computing systems. A number of image data sets derived from the raw spectral bands (smoothed data sets, band ratios and texture data sets) allows the student to examine and include these extracted variables in the classification process. Another data set consists of three registered LANDSAT image segments for three different dates so that the student may experiment with multitemporal data processing. The three LANDSAT scenes used in the class are described and the image data sets available for class exercises are listed.
Eyton, J. R.↗
Analysis and quantification of errors in the geometric correction of satellite images
The quantitative use of remote sensing satellite images in many applications requires that the geometric distortion inherent in these images be corrected, or rectified, to a desired map projection. The most widely used technique relies on ground control points to empirically determine a mathematical coordinate transformation to correct the geometry. In this paper, using the method of least squares, expressions for the accuracy of the geometric transformation and of the rectification of the satellite image to a map projection are derived. Explicit relations between the global accuracy of the transformation and the number, location, and local accuracy of the ground control points are obtained. The results are applied to the correction of a Landsat MSS image.
Ford, G. E.↗
Hierarchical Image Segmentation of Remotely Sensed Data using Massively Parallel GNU-LINUX Software
A hierarchical set of image segmentations is a set of several image segmentations of the same image at different levels of detail in which the segmentations at coarser levels of detail can be produced from simple merges of regions at finer levels of detail. In [1], Tilton, et a1 describes an approach for producing hierarchical segmentations (called HSEG) and gave a progress report on exploiting these hierarchical segmentations for image information mining. The HSEG algorithm is a hybrid of region growing and constrained spectral clustering that produces a hierarchical set of image segmentations based on detected convergence points. In the main, HSEG employs the hierarchical stepwise optimization (HSWO) approach to region growing, which was described as early as 1989 by Beaulieu and Goldberg. The HSWO approach seeks to produce segmentations that are more optimized than those produced by more classic approaches to region growing (e.g. Horowitz and T. Pavlidis, [3]). In addition, HSEG optionally interjects between HSWO region growing iterations, merges between spatially non-adjacent regions (i.e., spectrally based merging or clustering) constrained by a threshold derived from the previous HSWO region growing iteration. While the addition of constrained spectral clustering improves the utility of the segmentation results, especially for larger images, it also significantly increases HSEG s computational requirements. To counteract this, a computationally efficient recursive, divide-and-conquer, implementation of HSEG (RHSEG) was devised, which includes special code to avoid processing artifacts caused by RHSEG s recursive subdivision of the image data. The recursive nature of RHSEG makes for a straightforward parallel implementation. This paper describes the HSEG algorithm, its recursive formulation (referred to as RHSEG), and the implementation of RHSEG using massively parallel GNU-LINUX software. Results with Landsat TM data are included comparing RHSEG with classic region growing.
Tilton, James C.↗
Compensation for Spherical Geometric and Absorption Effects on Lower Thermospheric Emission Intensities Derived from High Earth Orbit Images
Remote sensing of the atmosphere from high earth orbit is very attractive due to the large field of view obtained and a true global perspective. This viewpoint is complicated by earth curvature effects so that slant path enhancement and absorption effects, small from low earth orbit, become dominant even at small nadir view angles. The effect is further complicated by the large range of local times and solar zenith angles in a single image leading to a modulation of the image intensity by a significant portion of the diurnal height variation of the absorbing layer. The latter effect is significant in particular for mesospheric, stratospheric and auroral emissions due to their depth in the atmosphere. As a particular case, the emissions from atomic oxygen (130.4 and 135.6 nm) and molecular nitrogen (two LBH bands, LBHS from 140 to 160 nm and LBHL from 160 to 180 nm) as viewed from the Ultraviolet Imager (UVI) are examined. The LBH emissions are of particular interest since LBHS has significant 02 absorption while LBHL does not, In the case of auroral emissions this differential absorption, well examined in the nadir, gives information about the height of the emission and therefore the energy of the precipitating particles. Using simulations of the viewing geometry and images from the UVI we examine these effects and obtain correction factors to adjust to the nadir case with a significant improvement of the derived characteristic energy. There is a surprisingly large effect on the images from the 02 diurnal layer height changes. An empirical compensation to the nadir case is explored based on the local nadir and local zenith angles for each portion of the image. These compensations are demonstrated as applied to the above emissions in both auroral and dayglow images and compared to models. The extension of these findings to other instruments, emissions and spectral regions is examined.
Swift, W.↗
Multispectral Snapshot Imagers Onboard Small Satellite Formations for Multi-Angular Remote Sensing
Multispectral snapshot imagers are capable of producing 2D spatial images with a single exposure at selected, numerous wavelengths using the same camera, therefore operate differently from push broom or whiskbroom imagers. They are payloads of choice in multi-angular, multi-spectral imaging missions that use small satellites flying in controlled formation, to retrieve Earth science measurements dependent on the targets Bidirectional Reflectance-Distribution Function (BRDF). Narrow fields of view are needed to capture images with moderate spatial resolution. This paper quantifies the dependencies of the imagers optical system, spectral elements and camera on the requirements of the formation mission and their impact on performance metrics such as spectral range, swath and signal to noise ratio (SNR). All variables and metrics have been generated from a comprehensive, payload design tool. The baseline optical parameters selected (diameter 7 cm, focal length 10.5 cm, pixel size 20 micron, field of view 1.15 deg) and snapshot imaging technologies are available. The spectral components shortlisted were waveguide spectrometers, acousto-optic tunable filters (AOTF), electronically actuated Fabry-Perot interferometers, and integral field spectrographs. Qualitative evaluation favored AOTFs because of their low weight, small size, and flight heritage. Quantitative analysis showed that waveguide spectrometers perform better in terms of achievable swath (10-90 km) and SNR (greater than 20) for 86 wavebands, but the data volume generated will need very high bandwidth communication to downlink. AOTFs meet the external data volume caps well as the minimum spectral (wavebands) and radiometric (SNR) requirements, therefore are found to be currently feasible in spite of lower swath and SNR.
Nag, Sreeja↗
Electron-bombarded CCD detectors for ultraviolet atmospheric remote sensing
Electronic image sensors based on charge coupled devices operated in electron-bombarded mode, yielding real-time, remote-readout, photon-limited UV imaging capability are being developed. The sensors also incorporate fast-focal-ratio Schmidt optics and opaque photocathodes, giving nearly the ultimate possible diffuse-source sensitivity. They can be used for direct imagery of atmospheric emission phenomena, and for imaging spectrography with moderate spatial and spectral resolution. The current state of instrument development, laboratory results, planned future developments and proposed applications of the sensors in space flight instrumentation is described.
Carruthers, G. R.↗
Airborne Remote Sensing
NASA imaging technology has provided the basis for a commercial agricultural reconnaissance service. AG-RECON furnishes information from airborne sensors, aerial photographs and satellite and ground databases to farmers, foresters, geologists, etc. This service produces color "maps" of Earth conditions, which enable clients to detect crop color changes or temperature changes that may indicate fire damage or pest stress problems.
Source record↗
AVIRIS: A New Age Approach to Earth Remote Sensing
An imaging spectrometer measures a contiguous spectrum of light for each spatial element of an image.
AVIRIS↗
Wavelength tunable liquid crystal imaging filters for remote sensing from geosynchronous platforms
Recent advances in liquid crystal technology have enabled us to construct tunable birefringent filters with bandwidths between approximately 0.1 nm and 50 nm. The center wavelength of these filters can be selected electronically, in a few tens of milliseconds, with no moving parts. These liquid crystal tunable filters (LCTF's), together with existing CCD detectors, make possible a new generation of lightweight, rugged, high-resolution imaging spectrophotometers. Such instruments would be particularly interesting for remote sensing applications from geosynchronous platforms. Important advantages exist in the aperture, absence of image shift, power consumption, size, weight, and absence of high drive frequencies, compared to current instruments used or considered for multispectral scene analysis. In the present work, we have reviewed spectral requirements of planned NASA geosynchronous remote sensing missions and identified several applications of the liquid crystal tunable filter technology. We have modeled the LCTF performance in the visible and near-infrared, and carried out a literature study on space-hardening of the filter components, to evaluate the suitability of LCTF's for geosynchronous missions. We have also compared the power consumption, weight, size, reliability, and optical performance of an imaging spectrophotometer using a LCTF monochromator, to other instruments that have been put forward for remote sensing from geosynchronous platforms. We put forward some conceptual designs for LCTF's that seem to offer important reliability, over the mechanical filter wheels presently baselined for the HEPI and ALM experiments. The extremely wide acceptance angle achievable with LCTF's could also avoid the present need for large-aperture interference filters in the ALM (and LIS) experiments. Thermal vacuum testing and radiation damage analysis is required to investigate the space hardening of these new filters for geosynchronous flight.
Foukal, Peter↗
NeMO-Net & Fluid Lensing: The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment Using Fluid Lensing Augmentation of NASA EOS Data
We present preliminary results from NASA NeMO-Net, the first neural multi-modal observation and training network for global coral reef assessment. NeMO-Net is an open-source deep convolutional neural network (CNN) and interactive active learning training software in development which will assess the present and past dynamics of coral reef ecosystems. NeMO-Net exploits active learning and data fusion of mm-scale remotely sensed 3D images of coral reefs captured using fluid lensing with the NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as hyperspectral airborne remote sensing data from the ongoing NASA CORAL mission and lower-resolution satellite data to determine coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. Aquatic ecosystems, particularly coral reefs, remain quantitatively misrepresented by low-resolution remote sensing as a result of refractive distortion from ocean waves, optical attenuation, and remoteness. Machine learning classification of coral reefs using FluidCam mm-scale 3D data show that present satellite and airborne remote sensing techniques poorly characterize coral reef percent living cover, morphology type, and species breakdown at the mm, cm, and meter scales. Indeed, current global assessments of coral reef cover and morphology classification based on km-scale satellite data alone can suffer from segmentation errors greater than 40%, capable of change detection only on yearly temporal scales and decameter spatial scales, significantly hindering our understanding of patterns and processes in marine biodiversity at a time when these ecosystems are experiencing unprecedented anthropogenic pressures, ocean acidification, and sea surface temperature rise. NeMO-Net leverages our augmented machine learning algorithm that demonstrates data fusion of regional FluidCam (mm, cm-scale) airborne remote sensing with global low-resolution (m, km-scale) airborne and spaceborne imagery to reduce classification errors up to 80% over regional scales. Such technologies can substantially enhance our ability to assess coral reef ecosystems dynamics.
satellite data↗