Trends and concerns in digital cartography

CISRG discussion paper ;

An intelligent Geographic Information System for design

Recent advances in geographic information systems (GIS) and artificial intelligence (AI) techniques have been summarised, concentrating on the theoretical aspects of their construction and use. Existing projects combining AI and GIS have also been discussed, with attention paid to the interfacing methods used and problems uncovered by the approaches. AI and GIS have been combined in this research to create an intelligent GIS for design. This has been applied to off-shore pipeline route design. The system was tested using data from a real pipeline design project. [Continues.

Probabilistic segmentation of remotely sensed images

For information extraction from image data to create or update geographic information systems, objects are identified and labeled using an integration of segmentation and classification. This yields geometric and thematic information, respectively.Bayesian image classifiers calculate class posterior probabilities on the basis of estimated class probability densities and prior probabilities. This thesis presents refined probability estimates, which are local, i.e pertain to image regions, rather than to the entire image. Local class probability densities are estimated in a non-parametric way with an extended k-Nearest Neighbor method. Iterative estimation of class mixing proportions in arbitrary image regions yields local prior probabilities.The improved estimates of prior probabilities and probability densities increase the reliability of posterior probabilities and enhance subsequent decision making, such as maximum posterior probability class selection. Moreover, class areas are estimated more accurately, compared to standard Maximum Likelihood classification.Two sources of image regionalization are distinguished. Ancillary data in geographic information systems often divide the image area into regions with different class mixing proportions, in which probabilities are estimated. Otherwise, a regionalization can be obtained by image segmentation. A region based method is presented, being a generalization of connected component labeling in the quadtree domain. It recursively merges leaves in a quadtree representation of a multi-spectral image into segments with arbitrary shapes and sizes. Order dependency is avoided by applying the procedure iteratively with slowly relaxing homogeneity criteria.Region fragmentation and region merging, caused by spectral variation within objects and spectral similarity between adjacent objects, are avoided by regarding class homogeneity in addition to spectral homogeneity. As expected, most terrain objects correspond to image segments. These, however, reside at different levels in a segmentation pyramid. Therefore, class mixing proportions are estimated in all segments of such a pyramid to distinguish between pure and mixed ones. Pure segments are selected at the highest possible level, which may vary over the image. They form a non-overlapping set of labeled objects without fragmentation or merging. In image areas where classes cannot be separated, because of spatial or spectral resolution limitations, mixed segments are selected from the pyramid. They form uncertain objects, to which a mixture of classes with known proportion is assigned.Subsequently, remotely sensed data are used for taking decisions in geographical information systems. These decisions are usually based on crisp classifications and, therefore, influenced by classification errors and uncertainties. Moreover, when processing spatial data for decision making, the objectives and preferences of the decision maker are crucial to deal with. This thesis proposes to exploit mathematical decision analysis for integrating uncertainties and preferences, on the basis of carefully estimated probabilistic class information. It aims to solve complex decision problems on the basis of remotely sensed data.</p

Lossless data compression of grid-based digital elevation models: a PNG image format evaluation

At present, computers, lasers, radars, planes and satellite technologies make possible very fast and accurate topographic data acquisition for the production of maps. However, the problem of managing and manipulating this data efficiently remains. One particular type of map is the elevation map. When stored on a computer, it is often referred to as a Digital Elevation Model (DEM). A DEM is usually a square matrix of elevations. It is like an image, except that it contains a single channel of information (that is, elevation) and can be compressed in a lossy or lossless manner by way of existing image compression protocols. Compression has the effect of reducing memory requirements and speed of transmission over digital links, while maintaining the integrity of data as required. In this context, this paper investigates the effects of the PNG (Portable Network Graphics) lossless image compression protocol on floating-point elevation values for 16-bit DEMs of dissimilar terrain characteristics. The PNG is a robust, universally supported, extensible, lossless, general-purpose and patent-free image format. Tests demonstrate that the compression ratios and run decompression times achieved with the PNG lossless compression protocol can be comparable to, or better than, proprietary lossless JPEG variants, other image formats and available lossless compression algorithms

Multi-scale data storage schemes for spatial information systems

This thesis documents a research project that has led to the design and prototype implementation of several data storage schemes suited to the efficient multi-scale representation of integrated spatial data. Spatial information systems will benefit from having data models which allow for data to be viewed and analysed at various levels of detail, while the integration of data from different sources will lead to a more accurate representation of reality. The work has addressed two specific problems. The first concerns the design of an integrated multi-scale data model suited for use within Geographical Information Systems. This has led to the development of two data models, each of which allow for the integration of terrain data and topographic data at multiple levels of detail. The models are based on a combination of adapted versions of three previous data structures, namely, the constrained Delaunay pyramid, the line generalisation tree and the fixed grid. The second specific problem addressed in this thesis has been the development of an integrated multi-scale 3-D geological data model, for use within a Geoscientific Information System. This has resulted in a data storage scheme which enables the integration of terrain data, geological outcrop data and borehole data at various levels of detail. The thesis also presents details of prototype database implementations of each of the new data storage schemes. These implementations have served to demonstrate the feasibility and benefits of an integrated multi-scale approach. The research has also brought to light some areas that will need further research before fully functional systems are produced. The final chapter contains, in addition to conclusions made as a result of the research to date, a summary of some of these areas that require future work

Parallel implementation of fractal image compression

Thesis (M.Sc.Eng.)-University of Natal, Durban, 2000.Fractal image compression exploits the piecewise self-similarity present in real images as a form of information redundancy that can be eliminated to achieve compression. This theory based on Partitioned Iterated Function Systems is presented. As an alternative to the established JPEG, it provides a similar compression-ratio to fidelity trade-off. Fractal techniques promise faster decoding and potentially higher fidelity, but the computationally intensive compression process has prevented commercial acceptance. This thesis presents an algorithm mapping the problem onto a parallel processor architecture, with the goal of reducing the encoding time. The experimental work involved implementation of this approach on the Texas Instruments TMS320C80 parallel processor system. Results indicate that the fractal compression process is unusually well suited to parallelism with speed gains approximately linearly related to the number of processors used. Parallel processing issues such as coherency, management and interfacing are discussed. The code designed incorporates pipelining and parallelism on all conceptual and practical levels ensuring that all resources are fully utilised, achieving close to optimal efficiency. The computational intensity was reduced by several means, including conventional classification of image sub-blocks by content with comparisons across class boundaries prohibited. A faster approach adopted was to perform estimate comparisons between blocks based on pixel value variance, identifying candidates for more time-consuming, accurate RMS inter-block comparisons. These techniques, combined with the parallelism, allow compression of 512x512 pixel x 8 bit images in under 20 seconds, while maintaining a 30dB PSNR. This is up to an order of magnitude faster than reported for conventional sequential processor implementations. Fractal based compression of colour images and video sequences is also considered. The work confirms the potential of fractal compression techniques, and demonstrates that a parallel implementation is appropriate for addressing the compression time problem. The processor system used in these investigations is faster than currently available PC platforms, but the relevance lies in the anticipation that future generations of affordable processors will exceed its performance. The advantages of fractal image compression may then be accessible to the average computer user, leading to commercial acceptance

The Space and Earth Science Data Compression Workshop

This document is the proceedings from a Space and Earth Science Data Compression Workshop, which was held on March 27, 1992, at the Snowbird Conference Center in Snowbird, Utah. This workshop was held in conjunction with the 1992 Data Compression Conference (DCC '92), which was held at the same location, March 24-26, 1992. The workshop explored opportunities for data compression to enhance the collection and analysis of space and Earth science data. The workshop consisted of eleven papers presented in four sessions. These papers describe research that is integrated into, or has the potential of being integrated into, a particular space and/or Earth science data information system. Presenters were encouraged to take into account the scientists's data requirements, and the constraints imposed by the data collection, transmission, distribution, and archival system