An image engineering system for the inspection of transparent construction materials

This article presents a modular photogrammetric recording and image analysis system for inspecting the material characteristics of transparent foils, in particular Ethylen–TetraFluorEthylen-Copolymer (ETFE) foils. The foils are put under increasing air pressure and are observed by a stereo camera system. Determining the time-variable 3D shape of transparent material imposes a number of challenges: especially the automatic point transfer between stereo images and, in temporal domain, from one image pair to the next. We developed an automatic approach that accommodates for these particular circumstances and allows reconstruction of the 3D shape for each epoch as well as determining 3D translation vectors between epochs by feature tracking. Examples including numerical results and accuracy measures prove the applicability of the system.     

Analysis of sorghum yield dependence on crop growth characteristics using noaa-avhrr data

Growth profiles of 1987-88 rabi sorghum crop cultivated in spatially extensive sites in six tehsils of Solapur and Ahmadnagar districts in Maharashtra have been generated using multidate NOAA AVHRR data based on crop growth equation suggested by Badhwar (1980). The sensitive parameters for sorghum yield modelling have been identified. The correlation of final grain yield with growth parameters shows that yield relationship is stronger when logarithmic senescence rate and timeintegrated logarithmic senescence rate are considered as the parameters instead of its value on any day during 30 days senescence period after attaining maximum vegetative cover.     

A comparative study of supervised classifiers on a subscene in Junagadh district,Gujarat

This paper describes the results of a comparative study of five classifiers viz., maximum likelihood, modified maximum likelihood, minimum distance to mean. Fisher and min-max, for classifying a subscene of Junagadh district using Landsat Thematic Mapper (TM) data. The kappa coefficient of agreement (k) and per cent correctly classified pixels for training data are used as measures of overall performance. It is observed that maximum likelihood and modified maximum likelihood classifiers perform better than the other three classifiers for this data set. Band combinations (3, 4, S) and (2, 3, 4, S) perform better than the usual combination (1,2,3,4), possibly because of presence of middle infrared band (band 5) on a scene dominated by vegetation cover. The band combination (1, 2, 3, 4, 5, 7) performed the best.     

Application of IRS-1A data for delineating buried channels in Southern part of Allahabad district of Uttar Pradesh

Two buried channels were identified in southern part of Allahabad district based on the visual interpretation of IRS-1 A LISS II FCC followed by detailed study of aerial photographs and cheks. It has been concluded on basis of the configuration of the channels that these were initially joined forming one channel which flowed from east to west although the present master slope of the area is from west to east. The present reversal of the drainage might have been caused by the neo-tectonic activity in the area. The buried channels provide potential ground water reservoirs in the area as proved by a few boreholes drilled on the channels. Some part of the area has become waterlogged due to the seepage of water from the canals cutting across one of the buried channels.     

Consideration of time-correlated errors in a Kalman filter applicable to GNSS

An algorithm for considering time-correlated errors in a Kalman filter is presented. The algorithm differs from previous implementations in that it does not suffer from numerical problems; does not contain inherent time latency or require reinterpretation of Kalman filter parameters, and gives full consideration to additive white noise that is often still present but ignored in previous implementations. Simulation results indicate that the application of the new algorithm yields more realistic and therefore useful state and covariance information than the standard implementation. Results from a field test of the algorithm applied to the problem of kinematic differential GPS demonstrate that the algorithm provides slightly pessimistic covariance estimates whereas the standard Kalman filter provides optimistic covariance estimates.     

Triangulated spherical splines for geopotential reconstruction

We present an alternate mathematical technique than contemporary spherical harmonics to approximate the geopotential based on triangulated spherical spline functions, which are smooth piecewise spherical harmonic polynomials over spherical triangulations. The new method is capable of multi-spatial resolution modeling and could thus enhance spatial resolutions for regional gravity field inversion using data from space gravimetry missions such as CHAMP, GRACE or GOCE. First, we propose to use the minimal energy spherical spline interpolation to find a good approximation of the geopotential at the orbital altitude of the satellite. Then we explain how to solve Laplace’s equation on the Earth’s exterior to compute a spherical spline to approximate the geopotential at the Earth’s surface. We propose a domain decomposition technique, which can compute an approximation of the minimal energy spherical spline interpolation on the orbital altitude and a multiple star technique to compute the spherical spline approximation by the collocation method. We prove that the spherical spline constructed by means of the domain decomposition technique converges to the minimal energy spline interpolation. We also prove that the modeled spline geopotential is continuous from the satellite altitude down to the Earth’s surface. We have implemented the two computational algorithms and applied them in a numerical experiment using simulated CHAMP geopotential observations computed at satellite altitude (450 km) assuming EGM96 (n _max = 90) is the truth model. We then validate our approach by comparing the computed geopotential values using the resulting spherical spline model down to the Earth’s surface, with the truth EGM96 values over several study regions. Our numerical evidence demonstrates that the algorithms produce a viable alternative of regional gravity field solution potentially exploiting the full accuracy of data from space gravimetry missions. The major advantage of our method is that it allows us to compute the geopotential over the regions of interest as well as enhancing the spatial resolution commensurable with the characteristics of satellite coverage, which could not be done using a global spherical harmonic representation. The results in this paper are based on the research supported by the National Science Foundation under the grant no. 0327577.     

Artificial Neural Network (ANN) Based Inversion of Benthic Substrate Bottom Type and Bathymetry in Optically Shallow Waters - Initial Model Results

Ocean-colour remote sensing in optically shallow waters is influenced by contribution from the water column depth as well as by the substrate type. Therefore, it is required to include the contribution from the water column and substrate bottom type for bathymetry estimation. In this report we demonstrate the use of Artificial Neural Network (ANN) based approach to spectrally distinguish various benthic bottom types and estimate depth of substrate bottom simultaneously in optically shallow waters. We have used in-water radiative transfer simulation modeling to generate simulated top-of-the-water column reflectance the four major benthic bottom types viz. sea grass, coral sand, green algae and red algae using Hydrolight simulation model. The simulated remote sensing reflectance, for the four benthic bottom types having benthic bottom depth up to 30 m were generated for moderately clear waters. A multi-layer perceptron (MLP) type neural network was trained using the simulated data. ANN based approach was used for classification of the benthic bottom type and simultaneous inversion of bathymetry. Simulated data was inverted to yield benthic bottom type classification with an accuracy of ~98% for the four benthic substrate types and the substrate depth were estimated with an error of 0% for sea grass, 1% for coral sand and 1–3% for green and red algae up to 25 m, whereas for substrate bottom deeper than 25 m depth the classification errors increased by 2–5% for three substrate bottom types except sea grass bottom type. The initial results are promising which needs validation using the in-situ measured remote sensing reflectance spectra for implementing further on satellite data.     

Evaluation of Almond (Amygdalus Scopartia) Habitat Using Multi-criteria Decision Making Method

Bolhasan Forest region with an area of 5,725?ha is located on east north of Dezful County, Iran. The region belongs to natural forests of Dezful. Considering the area is mainly covered by valuable species of Amygdalus Scopartia, its sustainable exploitation and development as well as restoration enjoys great importance. Study ahead aims at selection of suitable habitats for under studied species using Analytical Hierarchy Process (AHP). Therewith, the required thematic maps were imported in to GIS Software and final suitability map was prepared. The results indicated that around 2,119?ha (37%) out of all study area has high suitability for habitat of Amygdalus Scopartia. In the meanwhile, 1,603?ha [equal to 28%] is categorized as good suitability class and 2,003?ha [35%] has poor suitability.     

The impact of accelerometry on CHAMP orbit determination

 The contribution of the STAR accelerometer to the CHAMP orbit precision is evaluated and quantified by means of the following results: orbital fit to the satellite laser ranging (SLR) observations, GPS reduced-dynamic vs SLR dynamic orbit comparisons, and comparison of the measured to the modeled non-gravitational accelerations (atmospheric drag in particular). In each of the four test periods in 2001, five CHAMP arcs of 2 days' length were analyzed. The mean RMS-of-fit of the SLR observations of the orbits computed with STAR data or the non-gravitational force model were 11 and 24 cm, respectively. If the accelerometer calibration parameters are not known at least at the few percent level, the SLR orbit fit deteriorates. This was tested by applying a 10% error to the along-track scale factor of the accelerometer, which increased the SLR RMS-of-fit on average to 17 cm. Reference orbits were computed employing the reduced-dynamic technique with GPS tracking data. This technique yields the most accurate orbit positions thanks to the estimation of a large number of empirical accelerations, which compensate for dynamic modeling errors. Comparison of the SLR orbits, computed with STAR data or the non-gravitational force model, to the GPS-based orbits showed that the SLR orbits employing accelerometer observations are twice as accurate. Finally, comparison of measured to modeled accelerations showed that the level of geomagnetic activity is highly correlated with the atmospheric drag model error, and that the largest errors occur around the geomagnetic poles. Received: 7 May 2002 / Accepted: 18 November 2002 Correspondence to: S. Bruinsma Acknowledgments. The TIGCM results were obtained from the CEDAR database. This study was supported by the Centre National d'Etudes Spatiales (CNES). The referees are thanked for their helpful remarks and suggestions.