Showing papers in "IEEE Transactions on Geoscience and Remote Sensing in 1998"
TL;DR: An approach has been developed that involves the fit of a combination of three simple scattering mechanisms to polarimetric SAR observations, which is justified as a simplification of more complicated scattering models, which require many inputs to solve the forward scattering problem.
Abstract: An approach has been developed that involves the fit of a combination of three simple scattering mechanisms to polarimetric SAR observations. The mechanisms are canopy scatter from a cloud of randomly oriented dipoles, evenor double-bounce scatter from a pair of orthogonal surfaces with different dielectric constants and Bragg scatter from a moderately rough surface. This composite scattering model is used to describe the polarimetric backscatter from naturally occurring scatterers. The model is shown to describe the behavior of polarimetric backscatter from tropical rain forests quite well by applying it to data from NASA/Jet Propulsion Laboratory's (JPLs) airborne polarimetric synthetic aperture radar (AIRSAR) system. The model fit allows clear discrimination between flooded and nonflooded forest and between forested and deforested areas, for example. The model is also shown to be usable as a predictive tool to estimate the effects of forest inundation and disturbance on the fully polarimetric radar signature. An advantage of this model fit approach is that the scattering contributions from the three basic scattering mechanisms can be estimated for clusters of pixels in polarimetric SAR images. Furthermore, it is shown that the contributions of the three scattering mechanisms to the HH, HV, and VV backscatter can be calculated from the model fit. Finally, this model fit approach is justified as a simplification of more complicated scattering models, which require many inputs to solve the forward scattering problem.
2,079 citations
TL;DR: The proposed MODIS standard products for land applications are described along with the current plans for data quality assessment and product validation.
Abstract: The first Moderate Resolution Imaging Spectroradiometer (MODIS) instrument is planned for launch by NASA in 1998. This instrument will provide a new and improved capability for terrestrial satellite remote sensing aimed at meeting the needs of global change research. The MODIS standard products will provide new and improved tools for moderate resolution land surface monitoring. These higher order data products have been designed to remove the burden of certain common types of data processing from the user community and meet the more general needs of global-to-regional monitoring, modeling, and assessment. The near-daily coverage of moderate resolution data from MODIS, coupled with the planned increase in high-resolution sampling from Landsat 7, will provide a powerful combination of observations. The full potential of MODIS will be realized once a stable and well-calibrated time-series of multispectral data has been established. In this paper the proposed MODIS standard products for land applications are described along with the current plans for data quality assessment and product validation.
1,415 citations
TL;DR: Validation using airborne simulator images taken over playas and ponds in central Nevada demonstrates that, with proper atmospheric compensation, it is possible to meet the theoretical expectations of temperature/emissivity separation (TES), and ASTER's TES algorithm hybridizes three established algorithms.
Abstract: The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) scanner on NASA's Earth Observing System (EOS)-AM1 satellite (launch scheduled for 1998) will collect five bands of thermal infrared (TIR) data with a noise equivalent temperature difference (NE/spl Delta/T) of /spl les/0.3 K to estimate surface temperatures and emissivity spectra, especially over land, where emissivities are not known in advance. Temperature/emissivity separation (TES) is difficult because there are five measurements but six unknowns. Various approaches have been used to constrain the extra degree of freedom. ASTER's TES algorithm hybridizes three established algorithms, first estimating the normalized emissivities and then calculating emissivity band ratios. An empirical relationship predicts the minimum emissivity from the spectral contrast of the ratioed values, permitting recovery of the emissivity spectrum. TES uses an iterative approach to remove reflected sky irradiance. Based on numerical simulation, TES should be able to recover temperatures within about /spl plusmn/1.5 K and emissivities within about /spl plusmn/0.015. Validation using airborne simulator images taken over playas and ponds in central Nevada demonstrates that, with proper atmospheric compensation, it is possible to meet the theoretical expectations. The main sources of uncertainty in the output temperature and emissivity images are the empirical relationship between emissivity values and spectral contrast, compensation for reflected sky irradiance, and ASTER's precision, calibration, and atmospheric compensation.
1,268 citations
TL;DR: A new phase unwrapping method is described and tested, which starts from the fact that the phase differences of neighboring pixels can be estimated with a potential error that is an integer multiple of 2/spl pi/.
Abstract: Phase unwrapping is the reconstruction of a function on a grid given its values mod 2/spl pi/. Phase unwrapping is a key problem in all quantitative applications of synthetic aperture radar (SAR) interferometry, but also in other fields. A new phase unwrapping method, which is a different approach from existing techniques, is described and tested. The method starts from the fact that the phase differences of neighboring pixels can be estimated with a potential error that is an integer multiple of 2/spl pi/. This suggests the formulation of the phase unwrapping problem as a global minimization problem with integer variables. Recognizing the network structure underlying the problem makes for an efficient solution. In fact, it is possible to equate the phase unwrapping problem to the problem of finding the minimum cost flow on a network, for the solution of which there exist very efficient techniques. The tests performed on real and simulated interferometric SAR data confirm the validity of the approach.
1,205 citations
TL;DR: The authors solve the coherence optimization problem involving maximization of interferometric coherence and formulate a new coherent decomposition for polarimetric SAR interferometry that allows the separation of the effective phase centers of different scattering mechanisms.
Abstract: The authors examine the role of polarimetry in synthetic aperture radar (SAR) interferometry. They first propose a general formulation for vector wave interferometry that includes conventional scalar interferometry as a special case. Then, they show how polarimetric basis transformations can be introduced into SAR interferometry and applied to form interferograms between all possible linear combinations of polarization states. This allows them to reveal the strong polarization dependency of the interferometric coherence. They then solve the coherence optimization problem involving maximization of interferometric coherence and formulate a new coherent decomposition for polarimetric SAR interferometry that allows the separation of the effective phase centers of different scattering mechanisms. A simplified stochastic scattering model for an elevated forest canopy is introduced to demonstrate the effectiveness of the proposed algorithms. In this way, they demonstrate the importance of wave polarization for the physical interpretation of SAR interferograms. They investigate the potential of polarimetric SAR interferometry using results from the evaluation of fully polarimetric interferometric shuttle imaging radar (SIR)-C/X-SAR data collected during October 8-9, 1994, over the SE Baikal Lake Selenga delta region of Buriatia, Southeast Siberia, Russia.
1,013 citations
TL;DR: An overview of the as-built instrument characteristics and the application of MISR to remote sensing of the Earth is provided.
Abstract: The Multi-angle Imaging SpectroRadiometer (MISR) instrument is scheduled for launch aboard the first of the Earth Observing System (EOS) spacecraft, EOS-AM1. MISR will provide global, radiometrically calibrated, georectified, and spatially coregistered imagery at nine discrete viewing angles and four visible/near-infrared spectral bands. Algorithms specifically developed to capitalize on this measurement strategy will be used to retrieve geophysical products for studies of clouds, aerosols, and surface radiation. This paper provides an overview of the as-built instrument characteristics and the application of MISR to remote sensing of the Earth.
947 citations
TL;DR: ASTER will, for the first time, provide high-spatial resolution multispectral thermal infrared data from orbit and the highest spatial resolution surface spectral reflectance temperature and emissivity data of all of the EOS-AM1 instruments.
Abstract: The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is a research facility instrument provided by the Ministry of International Trade and Industry (MITI), Tokyo, Japan to be launched on NASA's Earth Observing System morning (EOS-AM1) platform in 1998. ASTER has three spectral hands in the visible near-infrared (VNIR), six bands in the shortwave infrared (SWIR), and five bands in the thermal infrared (TIR) regions, with 15-, 30-, and 90-m ground resolution, respectively. The VNIR subsystem has one backward-viewing band for stereoscopic observation in the along-track direction. Because the data will have wide spectral coverage and relatively high spatial resolution, it will be possible to discriminate a variety of surface materials and reduce problems in some lower resolution data resulting from mixed pixels. ASTER will, for the first time, provide high-spatial resolution multispectral thermal infrared data from orbit and the highest spatial resolution surface spectral reflectance temperature and emissivity data of all of the EOS-AM1 instruments. The primary science objective of the ASTER mission is to improve understanding of the local- and regional-scale processes occurring on or near the Earth's surface and lower atmosphere, including surface-atmosphere interactions. Specific areas of the science investigation include the following: (1) land surface climatology; (2) vegetation and ecosystem dynamics; (3) volcano monitoring; (4) hazard monitoring; (5) aerosols and clouds; (6) carbon cycling in the marine ecosystem; (7) hydrology; (8) geology and soil; and (9) land surface and land cover change. There are three categories of ASTER data: a global map, regional monitoring data sets, and local data sets to be obtained for requests from individual investigators.
885 citations
TL;DR: The Moderate Resolution Imaging Spectroradiometer (MODIS), with 36 bands and 0.5-km geometric instantaneous-fields-of-view (GIFOVs) at nadir, has completed system level testing and has been integrated onto the Earth Observing System (EOS)-AM1 spacecraft, which is slated for launch in 1998.
Abstract: The Moderate Resolution Imaging Spectroradiometer (MODIS), with 36 bands and 0.25-, 0.5-, and 1.0-km geometric instantaneous-fields-of-view (GIFOVs) at nadir, has completed system level testing and has been integrated onto the Earth Observing System (EOS)-AM1 spacecraft, which is slated for launch in 1998. Raytheon Santa Barbara Remote Sensing (SBRS), Goleta, CA, the MODIS developer, has performed extensive characterization and calibration measurements that have demonstrated a system that meets or exceeds most of NASA's demanding requirements. Based on this demonstrated capability, the MODIS Science Team, an international group of 28 land, ocean, atmosphere, and calibration remote-sensing scientists, has commenced delivery of algorithms that will routinely calculate 42 MODIS standard data products postlaunch. These products range from atmospheric aerosols, snow cover, and land and water surface temperature to leaf area index, ocean chlorophyll concentration, and sea ice extent, to name just a few. A description of the Science Team, including members' research interests and descriptions of their MODIS algorithms, can be found at the MODIS homepage (http://ltpwww.gsfc.nasa.gov/MODIS/MODIS.html). The MODIS system level testing included sufficient measurements in both ambient and thermal-vacuum environments to both demonstrate specification compliance and enable postlaunch implementation of radiometric calibration algorithms. The latter will include calculations to account for changes in response versus scan angle, response versus temperature, and response linearity. The system level tests also included performance verification of the onboard calibration systems, including the solar diffuser stability monitor (SDSM), the blackbody (BB), and the spectral radiometric calibration assembly (SRCA), which will enahle monitoring of MODIS performance postlaunch. Descriptions of these subsystems are also on the MODIS homepage.
823 citations
TL;DR: The Moderate Resolution Imaging Spectroradiometer will add a significant new capability for investigating the 70% of the Earth's surface that is covered by oceans, in addition to contributing to the continuation of a decadal scale time series necessary for climate change assessment in the oceans.
Abstract: The Moderate Resolution Imaging Spectroradiometer (MODIS) will add a significant new capability for investigating the 70% of the Earth's surface that is covered by oceans, in addition to contributing to the continuation of a decadal scale time series necessary for climate change assessment in the oceans. Sensor capabilities of particular importance for improving the accuracy of ocean products include high SNR and high stability for narrow or spectral bands, improved onboard radiometric calibration and stability monitoring, and improved science data product algorithms. Spectral bands for resolving solar-stimulated chlorophyll fluorescence and a split window in the 4-/spl mu/m region for SST will result in important new global ocean science products for biology and physics. MODIS will return full global data at 1-km resolution. The complete suite of Levels 2 and 3 ocean products is reviewed, and many areas where MODIS data are expected to make significant, new contributions to the enhanced understanding of the oceans' role in understanding climate change are discussed. In providing a highly complementary and consistent set of observations of terrestrial, atmospheric, and ocean observations, MODIS data will provide important new information on the interactions between Earth's major components.
507 citations
TL;DR: The results from false change analysis indicate a substantial degradation in the accuracy of remotely sensed change detection due to misregistration, and it is shown that a registration accuracy of less than one-fifth of a pixel is required to achieve a change detection error ofLess than 10%.
Abstract: Image misregistration has become one of the significant bottlenecks for improving the accuracy of multisource data analysis, such as data fusion and change detection. In this paper, the effects of misregistration on the accuracy of remotely sensed change detection were systematically investigated and quantitatively evaluated. This simulation research focused on two interconnected components. In the first component, the statistical properties of the multispectral difference images were evaluated using semivariograms when multitemporal images were progressively misregistered against themselves and each other to investigate the band, temporal, and spatial frequency sensitivities of change detection to image misregistration. In the second component, the ellipsoidal change detection technique, based on the Mahalanobis distance of multispectral difference images, was proposed and used to progressively detect the land cover transitions at each misregistration stage for each pair of multitemporal images. The impact of misregistration on change detection was then evaluated in terms of the accuracy of change detection using the output from the ellipsoidal change detector. The experimental results using Landsat Thematic Mapper (TM) imagery are presented. It is interesting to notice that, among the seven TM bands, band 4 (near-infrared channel) is the most sensitive to misregistration when change detection is concerned. The results from false change analysis indicate a substantial degradation in the accuracy of remotely sensed change detection due to misregistration. It is shown that a registration accuracy of less than one-fifth of a pixel is required to achieve a change detection error of less than 10%.
479 citations
TL;DR: The authors propose a two-step algorithm for almost unsupervised detection of linear structures, in particular, main axes in road networks, as seen in synthetic aperture radar (SAR) images.
Abstract: The authors propose a two-step algorithm for almost unsupervised detection of linear structures, in particular, main axes in road networks, as seen in synthetic aperture radar (SAR) images. The first step is local and is used to extract linear features from the speckle radar image, which are treated as road-segment candidates. The authors present two local line detectors as well as a method for fusing information from these detectors. In the second global step, they identify the real roads among the segment candidates by defining a Markov random field (MRF) on a set of segments, which introduces contextual knowledge about the shape of road objects. The influence of the parameters on the road detection is studied and results are presented for various real radar images.
TL;DR: A generic MODIS land gridding and compositing algorithm that takes advantage of the data storage structure and enables the exploitation of multiple observations of the surface more fully than conventional approaches is described.
Abstract: The methodology used to store a number of the Moderate Resolution Imaging Spectroradiometer (MODIS) land products is described. The approach has several scientific and data processing advantages over conventional approaches used to store remotely sensed data sets and may be applied to any remote-sensing data set in which the observations are geolocated to subpixel accuracy. The methodology will enable new algorithms to be more accurately developed because important information about the intersection between the sensor observations and the output grid cells are preserved. The methodology will satisfy the very different needs of the MODIS land product generation algorithms, allow sophisticated users to develop their own application-specific MODIS land data sets, and enable efficient processing and reprocessing of MODIS land products. A generic MODIS land gridding and compositing algorithm that takes advantage of the data storage structure and enables the exploitation of multiple observations of the surface more fully than conventional approaches is described. The algorithms are illustrated with simulated MODIS data, and the practical considerations of increased data storage are discussed.
TL;DR: Three algorithms are described that will be implemented to retrieve aerosol properties globally using MISR data, and results indicate that aerosol optical depth can be retrieved with an accuracy of 0.05 or 10%, whichever is greater, and some information can be obtained about the aerosol chemical and physical properties.
Abstract: Aerosols are believed to play a direct role in the radiation budget of Earth, but their net radiative effect is not well established, particularly on regional scales. Whether aerosols heat or cool a given location depends on their composition and column amount and on the surface albedo, information that is not routinely available, especially over land. Obtaining global information on aerosol and surface radiative characteristics, over both ocean and land, is a task of the Multi-angle Imaging SpectroRadiometer (MISR), an instrument to be launched in 1998 on the Earth Observing System EOS-AM1 platform. Three algorithms are described that will be implemented to retrieve aerosol properties globally using MISR data. Because of the large volume of data to be processed on a daily basis, these algorithms rely on lookup tables of atmospheric radiative parameters and predetermined aerosol mixture models to expedite the radiative transfer (RT) calculations. Over oceans, the "dark water" algorithm is used, taking full advantage of the nature of the MISR data. Over land, a choice of algorithms is made, depending on the surface types within a scene-dark water bodies, heavily vegetated areas, or high-contrast terrain. The retrieval algorithms are tested on simulated MISR data, computed using realistic aerosol and surface reflectance models. The results indicate that aerosol optical depth can be retrieved with an accuracy of 0.05 or 10%, whichever is greater, and some information can be obtained about the aerosol chemical and physical properties.
TL;DR: In this paper, the authors proposed a new generation of radar altimeter for Earth observation, with particular suitability for coastal ocean regions and polar ice sheets as well as open oceans.
Abstract: The key innovation in the delay/Doppler radar altimeter is delay compensation, analogous to range curvature correction in a burst-mode synthetic aperture radar (SAR). Following delay compensation, height estimates are sorted by Doppler frequency, and integrated in parallel. More equivalent looks are accumulated than in a conventional altimeter. The relatively small along-track footprint size is a constant of the system, typically on the order of 250 m for a Ku-band altimeter. The flat-surface response is an impulse rather than the more familiar step function produced by conventional satellite radar altimeters. The radar equation for the delay/Doppler radar altimeter has an h/sup -5/2/(CT)/sup 1/2/ dependence on height h and compressed pulse length /spl tau/, which is more efficient than the corresponding h/sup 3/CT factor for a pulse-limited altimeter. The radiometric response obtained by the new approach would be 10 dB stronger than that of the TOPEX/Poseidon altimeter, for example, if the same hardware were used in the delay/Doppler altimeter mode. This new technique leads to a smaller instrument that requires less power, yet performs better than a conventional radar altimeter. The concept represents a new generation of altimeter for Earth observation, with particular suitability for coastal ocean regions and polar ice sheets as well as open oceans.
TL;DR: An adaptive filtering algorithm based on an additive noise model that emphasizes filtering noise adaptively according to the local noise level and filtering along fringes using directionally dependent windows is developed and effective, especially for the tightly packed fringes of X-band interferometry.
Abstract: This paper addresses the noise filtering problem for synthetic aperture radar (SAR) interferometric phase images. The phase noise is characterized by an additive noise model. The model is verified with an L-band shuttle imaging radar (SIR)-C interferogram. An adaptive filtering algorithm based on this noise model is developed. It emphasizes filtering noise adaptively according to the local noise level and filtering along fringes using directionally dependent windows. This algorithm is effective, especially for the tightly packed fringes of X-band interferometry. Using simulated and SIR-C/X-SAR repeat-pass generated interferograms, the effectiveness of this filter is demonstrated by its capabilities in residue reduction, adaptive noise filtering, and its ability to filter areas with high fringe rates. In addition, a scheme of incorporating this filtering algorithm in iterative phase unwrapping using a least-squares method is proposed.
Langley Research Center1, Virginia Tech2, State University of New York System3, Oregon State University4, Royal Meteorological Institute5, National Oceanic and Atmospheric Administration6, École Polytechnique7, Goddard Space Flight Center8, University of California, San Diego9, Colorado State University10, University of Alabama in Huntsville11
TL;DR: These algorithms are a prototype for the system that will produce the scientific data required for studying the role of clouds and radiation in the Earth's climate system, and are fundamental to the ability to understand, detect, and predict global climate change.
Abstract: The Clouds and the Earth's Radiant Energy System (CERES) is part of NASA's Earth Observing System (EOS), CERES objectives include the following. (1) For climate change analysis, provide a continuation of the Earth Radiation Budget Experiment (ERBE) record of radiative fluxes at the top-of-the-atmosphere (TOA), analyzed using the same techniques as the existing ERBE data. (2) Double the accuracy of estimates of radiative fluxes at TOA and the Earth's surface. (3) Provide the first long-term global estimates of the radiative fluxes within the Earth's atmosphere. (4) Provide cloud property estimates collocated in space and time that are consistent with the radiative fluxes from surface to TOA. In order to accomplish these goals, CERES uses data from a combination of spaceborne instruments: CERES scanners, which are an improved version of the ERBE broadband radiometers, and collocated cloud spectral imager data on the same spacecraft. The CERES cloud and radiative flux data products should prove extremely useful in advancing the understanding of cloud-radiation interactions, particularly cloud feedback effects on the Earth's radiation balance. For this reason, the CERES data should be fundamental to the ability to understand, detect, and predict global climate change. CERES results should also be very useful for studying regional climate changes associated with deforestation, desertification, anthropogenic aerosols, and ENSO events. This overview summarizes the Release 3 version of the planned CERES data products and data analysis algorithms. These algorithms are a prototype for the system that will produce the scientific data required for studying the role of clouds and radiation in the Earth's climate system.
TL;DR: A new step-edge detector for SAR images is proposed, which is optimal in the minimum mean square error (MSSE) sense under a stochastic multiedge model and thresholding of the edge strength map by a modified version of the watershed algorithm and region merging to eliminate false edges complete an efficient segmentation scheme.
Abstract: Edge detection is a fundamental issue in image analysis. Due to the presence of speckle, which can be modeled as a strong, multiplicative noise, edge detection in synthetic aperture radar (SAR) images is extremely difficult, and edge detectors developed for optical images are inefficient. Several robust operators have been developed for the detection of isolated step edges in speckled images. The authors propose a new step-edge detector for SAR images, which is optimal in the minimum mean square error (MSSE) sense under a stochastic multiedge model. It computes a normalized ratio of exponentially weighted averages (ROEWA) on opposite sides of the central pixel. This is done in the horizontal and vertical direction, and the magnitude of the two components yields an edge strength map. Thresholding of the edge strength map by a modified version of the watershed algorithm and region merging to eliminate false edges complete an efficient segmentation scheme. Experimental results obtained from simulated SAR images as well as ERS-1 data are presented.
TL;DR: The authors develop and demonstrate a technique that allows the three-component velocity vector to be estimated from data acquired along two track directions under a surface-parallel flow assumption, and their results are promising.
Abstract: Satellite radar interferometry (SRI) provides an important new tool for determining ice-flow velocity. Interferometric measurements made from a single-track direction are sensitive only to a single component of the three-component velocity vector. Observations from along three different track directions would allow the full velocity vector to be determined. A north/south-looking synthetic aperture radar (SAR) could provide these observations over large portions of the globe, but not over large areas of the polar ice sheets. The authors develop and demonstrate a technique that allows the three-component velocity vector to be estimated from data acquired along two track directions (ascending and descending) under a surface-parallel flow assumption. This technique requires that there are accurate estimates of the surface slope, which are also determined interferometrically. To demonstrate the technique, the authors estimate the three-component velocity field for the Ryder Glacier, Greenland. Their results are promising, although they do not have yet ground-truth data with which to determine the accuracy of their estimates.
TL;DR: This model implements state-of-the-art Monte Carlo ray-tracing techniques and is dedicated to the study of light propagation in terrestrial environments as a virtual laboratory, where scenes of arbitrary complexity can be described explicitly and the relevant radiative processes can be represented in great detail at spatial scales relevant to simulate actual measurements.
Abstract: A model of radiation transfer in three-dimensional (3D) heterogeneous media is designed and evaluated. This model implements state-of-the-art Monte Carlo ray-tracing techniques and is dedicated to the study of light propagation in terrestrial environments. It is designed as a virtual laboratory, where scenes of arbitrary complexity can be described explicitly and where the relevant radiative processes can be represented in great detail, at spatial scales relevant to simulate actual measurements. The approach capitalizes on the existing understanding of the elementary radiative processes and recognizes that the major difficulty in accurately describing the radiation field after its interaction with a typical terrestrial scene results from the complexity of the structure and the diversity of the properties of the elements of the scene. The output of the model can be customized to address various scientific investigations, including the determination of absorption profiles or of light-scattering distributions. The performance of the model is evaluated through detailed comparisons with laboratory measurements of an artificial target as well as with other established reflectance models for plant canopies.
TL;DR: The proposed three subspace projection approaches are viewed as a posteriori OSP as opposed to OSP, where the abundances of spectral signatures are not known a priori but need to be estimated, a situation to which the OSP cannot be directly applied.
Abstract: An orthogonal subspace projection (OSP) method using linear mixture modeling was recently explored in hyperspectral image classification and has shown promise in signature detection, discrimination, and classification. In this paper, the OSP is revisited and extended by three unconstrained least squares subspace projection approaches, called signature space OSP, target signature space OSP, and oblique subspace projection, where the abundances of spectral signatures are not known a priori but need to be estimated, a situation to which the OSP cannot be directly applied. The proposed three subspace projection methods can be used not only to estimate signature abundance, but also to classify a target signature at subpixel scale so as to achieve subpixel detection. As a result, they can be viewed as a posteriori OSP as opposed to OSP, which can be thought of as a priori OSP. In order to evaluate these three approaches, their associated least squares estimation errors are cast as a signal detection problem ill the framework of the Neyman-Pearson detection theory so that the effectiveness of their generated classifiers can be measured by receiver operating characteristics (ROC) analysis. All results are demonstrated by computer simulations and Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data.
TL;DR: A Monte Carlo simulation concluded that, in order to measure the rms height and the correlation length with a precision of /spl plusmn/10%, the surface segment should be at least 40l long and 200l long, respectively, where l is the mean (or true) value of the surface correlation length.
Abstract: Whereas it is well known that electromagnetic scattering by a randomly rough surface is strongly influenced by the surface-height correlation function, it is not clear as to how long a surface-height profile is needed and at what interval it should be sampled to experimentally quantify the correlation function of a real surface. This paper presents the results of a Monte Carlo simulation conducted to answer these questions. It was determined that, in order to measure the rms height and the correlation length with a precision of /spl plusmn/10%, the surface segment should be at least 40l long and 200l long, respectively, where l is the mean (or true) value of the surface correlation length. Shorter segment lengths can be used if multiple segments are measured and then the estimated values are averaged. The second part of the study focused on the relationship between sampling interval and measurement precision. It was found that, in order to estimate the surface roughness parameters with a precision of /spl plusmn/5%, it is necessary that the surface be sampled at a spacing no longer than 0.2 of the correlation length.
TL;DR: The algorithms to retrieve surface directional reflectances, albedos, and selected biophysical parameters using MISR data are described and a summary list of the MISR surface products is included.
Abstract: Knowledge of the directional and hemispherical reflectance properties of natural surfaces, such as soils and vegetation canopies, is essential for classification studies and canopy model inversion. The Multi-angle Imaging SpectroRadiometer (MISR), an instrument to be launched in 1998 onboard the EOS-AM1 platform, will make global observations of the Earth's surface at 1.1-km spatial resolution, with the objective of determining the atmospherically corrected reflectance properties of most of the land surface and the tropical ocean. The algorithms to retrieve surface directional reflectances, albedos, and selected biophysical parameters using MISR data are described. Since part of the MISR data analyses includes an aerosol retrieval, it is assumed that the optical properties of the atmosphere (i.e. aerosol characteristics) have been determined well enough to accurately model the radiative transfer process. The core surface retrieval algorithms are tested on simulated MISR data, computed using realistic surface reflectance and aerosol models, and the sensitivity of the retrieved directional and hemispherical reflectances to aerosol type and column amount is illustrated. Included is a summary list of the MISR surface products.
TL;DR: A three-dimensional (3D) time-domain numerical scheme for simulation of ground penetrating radar (GPR) on dispersive and inhomogeneous soils with conductive loss is described, and an almost linear speedup is observed.
Abstract: A three-dimensional (3D) time-domain numerical scheme for simulation of ground penetrating radar (GPR) on dispersive and inhomogeneous soils with conductive loss is described. The finite-difference time-domain (FDTD) method is used to discretize the partial differential equations for time stepping of the electromagnetic fields. The soil dispersion is modeled by multiterm Lorentz and/or Debye models and incorporated into the FDTD scheme by using the piecewise-linear recursive convolution (PLRC) technique. The dispersive soil parameters are obtained by fitting the model to reported experimental data. The perfectly matched layer (PML) is extended to match dispersive media and used as an absorbing boundary condition to simulate an open space. Examples are given to verify the numerical solution and demonstrate its applications. The 3D PML-PLRC-FDTD formulation facilitates the parallelization of the code. A version of the code is written for a 32-processor system, and an almost linear speedup is observed.
TL;DR: The semivariogram textural measure provides a larger classification accuracy than a classifier based on a co-occurrence matrix for the microwave images and a smaller classification accuracy for the optical images.
Abstract: Semivariogram functions are compared to co-occurrence matrices for classification of digital image texture, and accuracy is assessed using test sites. Images acquired over the following six different spectral bands are used: 1) SPOT HRV, near infrared; 2) Landsat thematic mapper (TM), visible red; 3) India Remote Sensing (IRS) LISS-II, visible green; 4) Magellan, Venus, S-band microwave; 5) shuttle imaging radar (SIR)-C, X-band microwave; 6) SIR-C, L-band microwave. The semivariogram textural measure provides a larger classification accuracy than a classifier based on a co-occurrence matrix for the microwave images and a smaller classification accuracy for the optical images.
TL;DR: Modifications to the linear kernel bidirectional reflectance distribution function (BRDF) models from Roujean et al. and from Wanner et al are presented to synthesize the TIR optical properties of a scene pixel from laboratory component measurements to estimate scene optical properties over a wide spectral range.
Abstract: This paper presents modifications to the linear kernel bidirectional reflectance distribution function (BRDF) models from Roujean et al. and from Wanner et al. that extend the spectral range into the thermal infrared (TIR). The present authors application is to synthesize the TIR optical properties of a scene pixel from laboratory component measurements. The angular reflectance and emissivity are needed to convert the radiance of a pixel as measured from space to land-surface temperature. The kernel models will be applied to develop a look-up table for the MODIS land-surface temperature algorithm to estimate the spectral, angular scene emissivity from land cover classification. A shrub scene and a dense canopy scene illustrate qualitative differences in angular emissivity that would not be evident without the kernel model modifications. They conclude that the modified models provide a simple and efficient way to estimate scene optical properties over a wide spectral range.
TL;DR: The Backus-Gilbert inversion (BGI) technique and the scatterometer image-reconstruction (SIR) algorithm are investigated as possible methods for creating enhanced resolution images from SSM/I data.
Abstract: One of the limitations in using Special Sensor Microwave/Imager (SSM/I) data for land and vegetation studies is the relatively low-spatial resolution. To ameliorate this limitation, resolution-enhancement algorithms can be applied to the data. In this paper, the Backus-Gilbert inversion (BGI) technique and the scatterometer image-reconstruction (SIR) algorithm are investigated as possible methods for creating enhanced resolution images from SSM/I data. The two algorithms are compared via both the simulation and the actual SSM/I data. The algorithms offer similar resolution enhancement, though SIR requires significantly less computation. Sample results over two land regions of South America are presented.
TL;DR: The theory is an extension of the conventional straight-path SAR-to-SAR on an arbitrary curved path, and a general formulation for the curved SAR is applied to circular SAR geometry, which has two important features.
Abstract: This paper presents a theory and its experimental demonstration of an imaging technique based on three-dimensional (3D) space-time confocal imaging and circular synthetic aperture radar (SAR). The theory is an extension of the conventional straight-path SAR-to-SAR on an arbitrary curved path. Next, a general formulation for the curved SAR is applied to circular SAR geometry, which has two important features. First, it allows the maximum attainable resolution to be an the order of a wavelength. Second, it makes 3D confocal imaging possible, X-band (7-13 GHz) imaging experiments are conducted to demonstrate this technique.
TL;DR: The authors present Gibbs-Markov random field models as a powerful and robust descriptor of spatial information in typical remote-sensing image data as well as examples for both synthetic aperture radar (SAR) and optical data.
Abstract: For pt.I see ibid., p.1431-45 (1998). The authors present Gibbs-Markov random field (GMRF) models as a powerful and robust descriptor of spatial information in typical remote-sensing image data. This class of stochastic image models provides an intuitive description of the image data using parameters of an energy function. For the selection among several nested models and the fit of the model, the authors proceed in two steps of Bayesian inference. This procedure yields the most plausible model and its most likely parameters, which together describe the image content in an optimal way. Its additional application at multiple scales of the image enables the authors to capture all structures being present in complex remote-sensing images. The calculation of the evidences of various models applied to the resulting quasicontinuous image pyramid automatically detects such structures. The authors present examples for both synthetic aperture radar (SAR) and optical data.
TL;DR: A new multispectral algorithm for cloud shadow detection and removal in daytime AVHRR scenes over land using a combination of geometric and optical constraints derived from the pixel-by-pixel cross-track geometry of the scene and image analysis methods to detect cloud shadow is presented.
Abstract: Although the accurate detection of cloud shadow in AVHRR scenes is important for many atmospheric and terrestrial applications, relatively little work in this area has appeared in the literature. This paper presents a new multispectral algorithm for cloud shadow detection and removal in daytime AVHRR scenes over land. It uses a combination of geometric and optical constraints, derived from the pixel-by-pixel cross-track geometry of the scene and image analysis methods to detect cloud shadow. The procedure works well in tropical and midlatitude regions under varying atmospheric conditions (wet-dry) and with different types of terrain. Results also show that underdetected cloud shadow ran produce errors of 30-40% in observed reflectances for affected pixels. Moreover, radiative transfer calculations show that the effects of cloud shadow are comparable to or exceed those of aerosol contamination for affected pixels. The procedure is computationally efficient and hence could be used to produce improved weather forecast, land cover, and land analysis products. The method is not intended for use under conditions of poor solar illumination and/or poor viewing geometry.
TL;DR: Observed polarimetric signatures of two types of biological scatterers, insects and birds, are compared and explained with a simple scattering model and the authors discuss the implications of recognizing the biological scattaerers' type and the size of birds.
Abstract: The authors present observations of biological scatterers with a polarimetric weather radar. The radar has a pencil beam, a high power, and a wavelength of 10 cm. It transmits horizontally and vertically polarized waves alternately. The available polarimetric variables are differential reflectivity, differential phase, and correlation coefficient between orthogonally polarized returns. Two types of biological scatterers, insects and birds, are contrasted. Observed polarimetric signatures of these are compared and explained with a simple scattering model. Finally, the authors discuss the implications of recognizing the biological scatterers' type and the size of birds.