The design and the development of a hyperspectral and multispectral airborne mapping system

Flexible and cost-effective tools for rapid image acquisition and natural resource mapping are needed by land managers. This paper describes the hardware and software architecture of a low-cost system that can be deployed on a light aircraft for rapid data acquisition. The Hyperspectral and Multispectral Cameras for Airborne Mapping (HAMCAM) was designed and developed in the Geospatial Laboratory for Environmental Dynamics at the University of Idaho as a student-learning tool, and to enhance the existing curriculum currently offered. The system integrates a hyperspectral sensor with four multispectral cameras, an Inertial Navigation System (INS), a Wide Area Augmentation System (WAAS)-capable Global Positioning System (GPS), a data acquisition computer, and custom software for running the sensors in a variety of different modes. The outputs include very high resolution imagery obtained in four adjustable visible and near-infrared bands from the multispectral imager. The hyperspectral sensor acquires 240 spectral bands along 2.7 nm intervals within the 445–900 nm range. The INS provides aircraft pitch, roll and yaw information for rapid geo-registration of the imagery. This paper will discuss the challenges associated with the development of the system and the integration of components and software for implementation of this system for natural resource management applications. In addition, sample imagery acquired by the sensor will be presented.     

On a global integrated fundamental network

Editor's comment: This letter has been received fromDr. E.H. Knickmeyer, University of Calgary, Dep. of Surveying Engineering, 2500 University Dr. N. Calgary, Alberta, Canada T2N 1N4, in April 1988. A response to this letter has been written byDr. C. Boucher, who represents the Central Bureau ofIERS andIAG SSG 5.123. Similar letters, dealing with matters of interest for geodesy and for IAG will in the future be published in Bulletin Géodésique with an answer by the person or committee in IAG which is most closely related to or responsible for the matter being dealt with in the letter.     

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.     

On-the-fly server-based aerial imagery management system

Aerial images are valuable products when dealing with both geospatial and geotemporal analysis. Nowadays, they are widely used for many different purposes and by an extensive public, including private companies, official administrations and individual users. Although in the past few years there has been an increasing interest in showing all kinds of geographical information on the World Wide Web, access to aerial imagery and its dissemination are still difficult and lack flexibility. This paper introduces an aerial imagery management system based on client–server principles, operated so as to allow users quick and efficient queries, processing and management of huge sets of photogrammetric imagery stored on raster servers. It is a novel product that is ready to provide image-based cartographic data available in public and private digital warehouses, facilitating all the required visualisations and queries, as well as geometric and radiometric processing on the fly. This paper shows the design, system architecture and various functionalities of the system in a real-life scenario.     

The effects of spatial autoregressive dependencies on inference in ordinary least squares: a geometric approach

There is a common belief that the presence of residual spatial autocorrelation in ordinary least squares (OLS) regression leads to inflated significance levels in beta coefficients and, in particular, inflated levels relative to the more efficient spatial error model (SEM). However, our simulations show that this is not always the case. Hence, the purpose of this paper is to examine this question from a geometric viewpoint. The key idea is to characterize the OLS test statistic in terms of angle cosines and examine the geometric implications of this characterization. Our first result is to show that if the explanatory variables in the regression exhibit no spatial autocorrelation, then the distribution of test statistics for individual beta coefficients in OLS is independent of any spatial autocorrelation in the error term. Hence, inferences about betas exhibit all the optimality properties of the classic uncorrelated error case. However, a second more important series of results show that if spatial autocorrelation is present in both the dependent and explanatory variables, then the conventional wisdom is correct. In particular, even when an explanatory variable is statistically independent of the dependent variable, such joint spatial dependencies tend to produce “spurious correlation” that results in over-rejection of the null hypothesis. The underlying geometric nature of this problem is clarified by illustrative examples. The paper concludes with a brief discussion of some possible remedies for this problem.     

Spectral and Spatial Quality Analysis in Pan Sharpening Process

Image fusion is a process to obtain new images containing more information by combining images obtained same or different sensors. With most of the earth observation satellites, high spatial resolution panchromatic images and low spatial resolution multispectral images are obtained. As an example of image fusion ??pan sharpening?? is a process of combining of high spatial resolution panchromatic images and low spatial resolution multispectral images. At the end of the fusion process both high spatial and spectral resolution new images are obtained. In this study, panchromatic and multispectral images gathered from Ikonos were used. Panchromatic and multispectral images belonging to the same sensor were combined by using different image fusion methods. As pan sharpening methods Brovey transform, Modified IHS, Principal Component Analysis (PCA), Wavelet PC transform and Wavelet A Trous transformation methods were used. Quality of fused products was evaluated from the point of view of both visual and statistical criteria. While wavelet based methods are succesfull in terms of protection of spectral quality of original multispectral images, the colorbased and statistical methods are giving better results within the improvement of spatial content.     

Recent advances in accessibility research: Representation,methodology and applications

 In this article we examine recent advances in accessibility research and their implications for future studies. We base our discussion on three intersecting dimensions that are useful for evaluating the contribution of recent studies: representation, methodology and applications. Various examples are selected to show that research concerned with representation and methodological problem solving is often applied to issues of broad concern in policy and planning. It is, however, not clear that the simultaneous treatment of representation, methodological and application issues has ever been fully worked out. The questions raised in this article may serve as a foundation for addressing issues pertinent to accurate representation, improved model building, and more rigorous applications in accessibility research. Received: 9 December 2002 / Accepted: 10 February 2003     

Groebner-basis solution of the three-dimensional resection problem (P4P)

The three-dimensional (3-D) resection problem is usually solved by first obtaining the distances connecting the unknown point P{X,Y,Z} to the known points P_i{X_i,Y_i,Z_i}i=1,2,3 through the solution of the three nonlinear Grunert equations and then using the obtained distances to determine the position {X,Y,Z} and the 3-D orientation parameters {,, }. Starting from the work of the German J. A. Grunert (1841), the Grunert equations have been solved in several substitutional steps and the desire as evidenced by several publications has been to reduce these number of steps. Similarly, the 3-D ranging step for position determination which follows the distance determination step involves the solution of three nonlinear ranging (`Bogenschnitt') equations solved in several substitution steps. It is illustrated how the algebraic technique of Groebner basis solves explicitly the nonlinear Grunert distance equations and the nonlinear 3-D ranging (`Bogenschnitt') equations in a single step once the equations have been converted into algebraic (polynomial) form. In particular, the algebraic tool of the Groebner basis provides symbolic solutions to the problem of 3-D resection. The various forward and backward substitution steps inherent in the classical closed-form solutions of the problem are avoided. Similar to the Gauss elimination technique in linear systems of equations, the Groebner basis eliminates several variables in a multivariate system of nonlinear equations in such a manner that the end product normally consists of a univariate polynomial whose roots can be determined by existing programs e.g. by using the roots command in Matlab.Acknowledgments.The first author wishes to acknowledge the support of JSPS (Japan Society of Promotion of Science) for the financial support that enabled the completion of the write-up of the paper at Kyoto University, Japan. The author is further grateful for the warm welcome and the good working atmosphere provided by his hosts Professors S. Takemoto and Y. Fukuda of the Department of Geophysics, Graduate School of Science, Kyoto University, Japan.     

Conjoint modeling of residential group preferences: A comparison of the internal validity of hierarchical information integration approaches

 In this paper, two approaches for measuring residential group preferences, based on the method of Hierarchical Information Integration (HII), are compared. In particular, the hypothesis that group-based preference models estimated from integrated HII experiments better predict group preferences than part individual-based group models estimated from classical HII experiments is tested. To that effect, the models' ability to predict group preferences for new residential alternatives is compared in a study of residential preferences of co-ops. Results indicate that integrated HII group experiments indeed result in better predictions of residential preferences.