Analysis of Interpolation Methods for Kinematic DGPS Control in Aerial Photogrammetry

Kinematic differential Global Positioning System (DGPS) positioning is routinely used in industry for directly observing an aircraft's position at each instant of photographic exposure during a photogammetric survey. A critical aspect of the subsequent data processing is estimation of the aircraft position at the exact time of exposure. GPS measurements are acquired at a uniform sampling rate, typically 1 Hz. The exposure times, however, do not generally coincide with these times. As a result, the exposure station positions must be interpolated from the adjacent GPS positions. This is typically done using a low-order polynomial, expressed as a function of time, for each coordinate dimension. However, trajectory perturbations induced by atmospheric turbulence can render such interpolation methods ineffective. This article will convey the results of an investigation into the use of several different interpolation models with airborne GPS data during straight, level flight. The fundamental task of time series reconstruction will first be addressed, in which several possible interpolation models are described. Two 10-Hz, airborne GPS data sets were collected to test the accuracy of each model. The error properties resulting from the application of each model to these data will be presented and analyzed in terms of time-domain statistics and frequency-domain characteristids. It will be demonstrated that interpolation error can be significantly reduced, especially in the height dimension, through judicious choice of an interpolator. ? 2000 John Wiley & Sons, Inc.     

Evaluation of Information Loss in Digital Elevation Models With Digital Photogrammetric Systems

Information loss is caused when a surface is sampled with a finite interval, such as in the production of a digital elevation model (DEM). This information loss can become the dominant part of the error in a DEM. The ability to quantify information loss enables guidance to be provided for an appropriate choice of grid interval and better accuracy assessment for the DEM. With the use of digital photogrammetric systems, evaluation of information loss has become much easier. This paper describes three methods of evaluating information loss. An example is given of the method which is most appropriate for use with a digital photogrammetric system, based on rock cliff surface data and the VirtuoZo system.     

Estimates of current status of forest types in Kolli Hill using remote sensing

The vegetation of Kolli Hill, has been classified for its forest cover types using landsat TM FCCs of two season namely summer (March) and winter (November). The FCCs of two seasons were interpreted visually based on the standard interpretation elements. Extensive field checks were done and corrections were made in both the maps wherever found necessary’. Finally the forest cover type map of Kolli Hill on 1:50,000 scale was drawn by overlaying the interpreted maps of the two seasons The different types of forest were named following Champion and Seth’s classification scheme and the areas of different forest types estimated.     

Non-stationary Approaches for Mapping Terrain and Assessing Prediction Uncertainty

It is well known that terrain may vary markedly over small areas and that statistics used to characterise spatial variation in terrain may be valid only over small areas. In geostatistical terminology, a non-stationary approach may be considered more appropriate than a stationary approach. In many applications, local variation is not accounted for sufficiently. This paper assesses potential benefits in using non-stationary geostatistical approaches for interpolation and for the assessment of uncertainty in predictions with implications for sampling design. Two main non-stationary approaches are employed in this paper dealing with (1) change in the mean and (2) change in the variogram across the region of interest. The relevant approaches are (1) kriging with a trend model (KT) using the variogram of residuals from local drift and (2) locally-adaptive variogram KT, both applied to a sampled photogrammetrically derived digital terrain model (DTM). The fractal dimension estimated locally from the double-log variogram is also mapped to illustrate how spatial variation changes across the data set. It is demonstrated that estimation of the variogram of residuals from local drift is worthwhile in this case for the characterisation of spatial variation. In addition, KT is shown to be useful for the assessment of uncertainty in predictions. This is shown to be true even when the sample grid is dense as is usually the case for remotely-sensed data. In addition, both ordinary kriging (OK) and KT are shown to provide more accurate predictions than inverse distance weighted (IDW) interpolation, used for comparative purposes.     

The Integration of Cooperation Model and Genetic Algorithm

In the photogrammetry,some researchers have applied genetic algorithms in aerial image texture classification and reducing hyper-spectrum remote sensing data.Genetic algorithm can rapidly find the solutions which are close to the optimal solution.But it is not easy to find the optimal solution.In order to solve the problem,a cooperative evolution idea integrating genetic algorithm and ant colony algorithm is presented in this paper.On the basis of the advantages of ant colony algorithm,this paper proposes the method integrating genetic algorithms and ant colony algorithm to overcome the drawback of genetic algorithms.Moreover,the paper takes designing texture classification masks of aerial images as an example to illustrate the integration theory and procedures.     

Shape Similarity measures of Linear Entities

The essential of feature matching technology lies in how to measure the similarity of spatial entities.Among all the possible similarity measures,the shape similarity measure is one of the most important measures because it is easy to collect the necessary parameters and it is also well matched with the human intuition.In this paper a new shape similarity measure of linear entities based on the differences of direction change along each line is presented and its effectiveness is illustrated.     

COMBINED GPS/GLONASS PRECISE POSITIONING FOR LONG-DISTANCE BASELINES

1　ＩｎｔｒｏｄｕｃｔｉｏｎＴｈｅｃｏｍｂｉｎｅｄＧＰＳ/ＧＬＯＮＡＳＳｏｆｆｅｒｓｍａｎｙａｄ ｖａｎｔａｇｅｓｃｏｍｐａｒｅｄｗｉｔｈＧＰＳ＿ｏｎｌｙｕｓｅｆｏｒｐｏｓｉｔｉｏｎ ｉｎｇａｐｐｌｉｃａｔｉｏｎｓｅｓｐｅｃｉａｌｌｙｉｎａｒｅａｓｗｈｅｒｅｔｈｅｎｕｍ ｂｅｒｏｆｖｉｓｉｂｌｅｓａｔｅｌｌｉｔｅｓｉｓｌｉｍｉｔｅｄ .ＴｈｅｉｎｃｌｕｓｉｏｎｏｆｔｈｅＧＬＯＮＡＳＳｓｉｇｎａｌｓｃａｎｉｎｃｒｅａｓｅｔｈｅａｃｃｕｒａｃｙｏｆｐｏｓｉｔｉｏｎｉｎｇａｓｗｅｌｌａｓｔｈｅａｖａｉｌａｂ…     

A FRAMEWORK FOR AUTOMATED CHANGE DETECTION SYSTEM

1Ｃｕｒｒｅｎｔｃｈａｎｇｅｄｅｔｅｃｔｉｏｎｔｅｃｈ ｎｉｑｕｅｓＡｕｔｏｍａｔｉｃｃｈａｎｇｅｄｅｔｅｃｔｉｏｎｉｎｉｍａｇｅｓｏｆａｇｉｖｅｎｓｃｅｎｅａｃｑｕｉｒｅｄａｔｄｉｆｆｅｒｅｎｔｔｉｍｅｓｉｓｏｎｅｏｆｔｈｅｍｏｓｔｉｎｔｅｒｅｓｔｉｎｇｔｏｐｉｃｓｏｆｉｍａｇｅｐｒｏｃｅｓｓｉｎｇ .Ｉｔｆｉｎｄｓｉｍ ｐｏｒｔａｎｔａｐｐｌｉｃａｔｉｏｎｓｗｉｔｈｉｎｄｉｆｆｅｒｅｎｔｃｏｎｔｅｘｔｓ,ｒａｎｇ ｉｎｇｆｒｏｍｖｉｓｕａｌｓｕｒｖｅｉｌｌａｎｃｅａｎｄｖｉｄｅｏｃｏｄｉｎｇｔｏｔ…     

PERFORMANCE ANALYSIS OF INTEGRATED GPS/GLONASS CARRIER PHASE-BASED POSITIONING

1　ＩｎｔｒｏｄｕｃｔｉｏｎＲｅａｌ＿ｔｉｍｅｋｉｎｅｍａｔｉｃＧＰＳｐｒｅｃｉｓｅｐｏｓｉｔｉｏｎｉｎｇｈａｓｂｅｅｎｐｌａｙｉｎｇａｎｉｎｃｒｅａｓｉｎｇｒｏｌｅｉｎｂｏｔｈｓｕｒｖｅｙｉｎｇａｎｄｎａｖｉｇａｔｉｏｎ ,ａｎｄｈａｓｂｅｃｏｍｅａｎｅｓｓｅｎｔｉａｌｔｏｏｌｆｏｒｐｒｅｃｉｓｅｒｅｌａｔｉｖｅｐｏｓｉｔｉｏｎｉｎｇ .Ｈｏｗｅｖｅｒ,ｒｅｌｉａｂｌｅａｎｄｃｏｒｒｅｃｔａｍｂｉｇｕｉｔｙｒｅｓｏｌｕｔｉｏｎｄｅｐｅｎｄｓｏｎｏｂｓｅｒｖａ ｔｉｏｎｓｕｐｏｎａｌａｒｇｅｎｕｍｂｅ…     

Improvements in height datum transfer expected from the GOCE mission

 One of the aims of the Earth Explorer Gravity Field and Steady-State Ocean Circulation (GOCE) mission is to provide global and regional models of the Earth's gravity field and of the geoid with high spatial resolution and accuracy. Using the GOCE error model, simulation studies were performed in order to estimate the accuracy of datum transfer in different areas of the Earth. The results showed that with the GOCE error model, the standard deviation of the height anomaly differences is about one order of magnitude better than the corresponding value with the EGM96 error model. As an example, the accuracy of the vertical datum transfer from the tide gauge of Amsterdam to New York was estimated equal to 57 cm when the EGM96 error model was used, while in the case of GOCE error model this accuracy was increased to 6 cm. The geoid undulation difference between the two places is about 76.5 m. Scaling the GOCE errors to the local gravity variance, the estimated accuracy varied between 3 and 7 cm, depending on the scaling model. Received: 1 March 2000 / Accepted: 21 February 2001