Contextual Spatiospectral Postreconstruction of Cloud-Contaminated Images

A general method has been proposed recently for the contextual reconstruction of cloud-contaminated areas in multitemporal multispectral images. It is based on the idea of making the prediction process learn from information available in the cloud-free neighborhood of contaminated areas. Though promising, this method does not fully exploit all available information, thus leaving room for further methodological enhancements. This letter presents a postreconstruction methodology for improving the contextual reconstruction process by opportunely capturing spatial and spectral correlations characterizing the considered image. In addition, we propose a solution to a problem that has not yet been addressed in the remote sensing literature, i.e., the generation of an error map beside the reconstructed images to provide end-users with helpful indications about reconstruction reliability. Thorough experiments conducted on a multitemporal sequence of Landsat-7 ETM+ images are reported and discussed.     

Accuracy assessment of lidar-derived digital elevation models

Despite the relatively high cost of airborne lidar-derived digital elevation models (DEMs), such products are usually presented without a satisfactory associated estimate of accuracy. For the most part, DEM accuracy estimates are typically provided by comparing lidar heights against a finite sample of check point coordinates from an independent source of higher accuracy, supposing a normal distribution of the derived height differences or errors. This paper proposes a new methodology to assess the vertical accuracy of lidar DEMs using confidence intervals constructed from a finite sample of errors computed at check points. A non-parametric approach has been tested where no particular error distribution is assumed, making the proposed methodology especially applicable to non-normal error distributions of the type usually found in DEMs derived from lidar. The performance of the proposed model was experimentally validated using Monte Carlo simulation on 18 vertical error data-sets. Fifteen of these data-sets were computed from original lidar data provided by the International Society for Photogrammetry and Remote Sensing Working Group III/3, using their respective filtered reference data as ground truth. The three remaining data-sets were provided by the Natural Environment Research Council's Airborne Research and Survey Facility lidar system, together with check points acquired using high precision kinematic GPS. The results proved promising, the proposed models reproducing the statistical behaviour of vertical errors of lidar using a favourable number of check points, even in the cases of data-sets with non-normally distributed residuals. This research can therefore be considered as a potentially important step towards improving the quality control of lidar-derived DEMs.     

GPS observations of the ionospheric F2-layer behavior during the 20th November 2003 geomagnetic storm over South Korea

The ionospheric F2-layer peak density (NmF2) and its height (hmF2) are of great influence on the shape of the ionospheric electron density profile Ne (h) and may be indicative of other physical processes within the ionosphere, especially those due to geomagnetic storms. Such parameters are often estimated using models such as the semiempirical international reference ionosphere (IRI) models or are measured using moderately priced to expensive instrumentation, such as ionosondes or incoherent scatter radars. Global positioning system (GPS) observations have become a powerful tool for mapping high-resolution ionospheric structures, which can be used to study the ionospheric response to geomagnetic storms. In this paper, we describe how 3-D ionospheric electron density profiles were produced from data of the dense permanent Korean GPS network using the tomography reconstruction technique. These profiles are verified by independent ionosonde data. The responses of GPS-derived parameters at the ionospheric F2-layer to the 20th November 2003 geomagnetic storm over South Korea are investigated. A fairly large increase in the electron density at the F2-layer peak (the NmF2) (positive storm) has been observed during this storm, which is accompanied by a significant uplift in the height of the F2 layer peak (the hmF2). This is confirmed by independent ionosonde observations. We suggest that the F2-layer peak height uplift and NmF2 increase are mainly associated with a strong eastward electric field, and are not associated with the increase of the O/N₂ ratio obtained from the GUVI instruments aboard the TIMED satellite. It is also inferred that the increase in NmF2 is not caused by the changes in neutral composition, but is related to other nonchemical effects, such as dynamical changes of vertical ion motions induced by winds and E × B drifts, tides and waves in the mesosphere/lower thermosphere region, which can be dynamically coupled upward to generate ionospheric perturbations and oscillations.     

Unsupervised Change Detection From Multichannel SAR Images

Multichannel synthetic aperture radar (SAR) data present a good potential for environmental monitoring and disaster management, owing both to their insensitivity to atmospheric and sun-illumination conditions, and to the improved discrimination capability they may provide as compared to single-channel SAR. However, this requires accurate and possibly automatic techniques to generate change maps from multichannel SAR images acquired from the same geographic area at different times. In this letter, an automatic unsupervised contextual change-detection method is proposed for two-date multichannel SAR images, by integrating a SAR-specific extension of the Fisher transform with a variant of the expectation-maximization algorithm and with Markov random fields. The method is validated by experiments on SIR-C/XSAR data     

High-Resolution Forecast Models of Water Vapor Over Mountains: Comparison With MERIS and Meteosat Data

Propagation delay due to variable tropospheric water vapor (WV) is one of the most intractable problems for radar interferometry, particularly over mountains. The WV field can be simulated by an atmospheric model, and the difference between the two fields is used to correct the radar interferogram. Here, we report our use of the U.K. Met Office Unified Model in a nested mode to produce high-resolution forecast fields for the 3-km-high Mount Etna volcano. The simulated precipitable-water field is validated against that retrieved from the Medium-Resolution Imaging Spectrometer (MERIS) radiometer on the Envisat satellite, which has a resolution of 300 m. Two case studies, one from winter (November 24, 2004) and one from summer (June 25, 2005), show that the mismatch between the model and the MERIS fields ( rms = 1.1 and 1.6 mm, respectively) is small. One of the main potential sources of error in the models is the timing of the WV field simulation. We show that long-wavelength upper tropospheric troughs of low WV could be identified in both the model output and Meteosat WV imagery for the November 24, 2004 case and used to choose the best time of model output.     

Motion Analysis in SAR Images of Unfocused Objects Using Time–Frequency Methods

Synthetic aperture radar (SAR) image formation processing assumes that the scene is stationary, and to focus an object, one coherently sums a large number of independent returns. Any target motion introduces phases that distort and/or translate the target's image. Target motion produces a smear primarily in the azimuth direction of the SAR image. Time-frequency (TF) modeling is used to analyze and correct the residual phase distortions. An interactive focusing algorithm based on TF modeling demonstrates how to correct the phase and to rapidly focus the mover. This is demonstrated on two watercraft observed in a SAR image. Then, two time-frequency representations (TFRs) are applied to estimate the motion parameters of the movers or refocus them or both. The first is the short-time Fourier transform, from which a velocity profile is constructed based on the length of the smear. The second TFR is the time-frequency distribution series, which is a robust derivative of the Wigner-Ville distribution that works well in this SAR environment. The smear is a modulated chirp, from which a velocity profile is plotted and the phase corrections are integrated to focus the movers. The relationship between these two methods is discussed. Both methods show good agreement on the example.     

Testing satellite and ground thermal imaging of low-temperature fumarolic fields: The dormant Nisyros Volcano (Greece)

The Nisyros Volcano (Greece) was monitored by satellite and ground thermal imaging during the period 2000–2002. Three night-scheduled Landsat-7 ETM⁺ thermal (band 6) images of Nisyros Island were processed to obtain land surface temperature. Ground temperature data were also collected during one of the satellite overpasses. Processed results involving orthorectification and 3-D atmospheric correction clearly show the existence of a thermal anomaly inside the Nisyros Caldera. This anomaly is associated mainly with the largest hydrothermal craters and has land surface temperatures 5–10 °C warmer than its surroundings. The ground temperature generally increased by about 4 °C inside the main crater over the period 2000–2002. Ground thermal images of the hydrothermal Stephanos Crater were also collected in 2002 using a portable thermal infrared camera. These images were calibrated to ground temperature data and orthorectified. A difference of about 0–2 °C was observed between the ground thermal images and the ground temperature data. The overall study demonstrates that satellite remote sensing of low-temperature fumarolic fields within calderas can provide a reliable long-term monitoring tool of dormant volcanoes that have the potential to reactivate. Similarly, a portable thermo-imager can easily be deployed for real-time monitoring using telemetric data transfer. The operational costs for both systems are relatively low for an early warning system.     

Best prediction in linear models with mixed integer/real unknowns: theory and application

In this contribution, we extend the existing theory of minimum mean squared error prediction (best prediction). This extention is motivated by the desire to be able to deal with models in which the parameter vectors have real-valued and/or integer-valued entries. New classes of predictors are introduced, based on the principle of equivariance. Equivariant prediction is developed for the real-parameter case, the integer-parameter case, and for the mixed integer/real case. The best predictors within these classes are identified, and they are shown to have a better performance than best linear (unbiased) prediction. This holds true for the mean squared error performance, as well as for the error variance performance. We show that, in the context of linear model prediction, best predictors and best estimators come in pairs. We take advantage of this property by also identifying the corresponding best estimators. All of the best equivariant estimators are shown to have a better precision than the best linear unbiased estimator. Although no restrictions are placed on the probability distributions of the random vectors, the Gaussian case is derived separately. The best predictors are also compared with least-squares predictors, in particular with the integer-based least-squares predictor introduced in Teunissen (J Geodesy, in press, 2006).     

On the link between GPS pseudorange noise and day-boundary discontinuities in geodetic time transfer solutions

When neglecting calibration issues, the accuracy of GPS-based time and frequency transfer using a combined analysis of code and carrier phase measurements highly depends on the noise of the GPS codes. In particular, the pseudorange noise is responsible for day-boundary discontinuities which can reach more than 1 ns in the time transfer results obtained from geodetic analysis. These discontinuities are caused by the fact that the data are analyzed in daily data batches where the absolute clock offset is determined by the mean code value during the daily data batch. This pseudorange noise is not a white noise, in particular due to multipath and variations of instrumental delays. In this paper, the pseudorange noise behavior is characterized in order to improve the understanding of the origin of the large day-boundary discontinuities in the geodetic time transfer results. In a first step, the effect of short-term noise and multipath is estimated, and shown to be responsible for only a maximum of 150 ps (picoseconds) of the day-boundary jumps, with only one exception at NRC1 where the correction provides a jump reduction of 300 ps. In a second step, a combination of time transfer results obtained with pseudoranges only and geodetic time transfer results is used to characterize the long-term evolution of pseudorange errors. It demonstrates that the day-boundary jumps, especially those of large amplitude, can be explained by an instrumental effect imposing a common behavior on all the satellite pseudoranges. Using known influences as temperature variations at ALGO or cable damages at HOB2, it is shown that the approach developed in this study can be used to look for the origin of the day-boundary discontinuities in other stations.