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Geography and cartography computing

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  • 1995 [6]

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Automatic map reading for distribution system design
Author(s): D.W. Auckland, I.E.D. Pickup, C. Zhou
Source: IEE Proceedings - Generation, Transmission and Distribution, Volume 142, Issue 6, 1995 , p. 549 - 554
Type: Article
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The paper describes how an architect's map is understood and used to give an electrical equivalent indicating the position and magnitude of electrical loads. The work shows that all of the houses on a map can be recognised, even for occluded and noisy cases, by the use of several basic image processing techniques. The road layout, which consists essentially of long narrow strips, is recognised also. This information may be passed directly to the distribution system design software.

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Texture analysis of terrain images using a Markov random field model
Author(s): M.C. Musgrave, A.P. Quinn
Source: Fifth International Conference on Image Processing and its Applications, 1995, p. 781 - 785
Type: ConferencePaper
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An approach to determining the textural attributes of an image an a pixel-by-pixel basis is presented. The technique is based on modelling the spatial interactions within the image using a Gaussian Markov random field. The method is applied to the classification of homogeneous texture images, artificial texture collages and a composite-texture SAR terrain image. The capabilities and limitations of the technique are considered in the light of these classification experiments.

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Global analysis of scanned maps for an automatic interpretation
Author(s): P. Guerin, M.P. Deseilligny
Source: Fifth International Conference on Image Processing and its Applications, 1995, p. 80 - 84
Type: ConferencePaper
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Automatic interpretation and digitization of scanned maps is an active field of investigation, since the data included in maps are needed for the constitution of geographical information systems or to improve the analysis of far more complex documents like satellite images and radar data. The authors deal with automatic interpretation of aerial images, for the constitution of 1:25,000 scale maps. One comes up against a diversity of contexts to handle and a complexity of information to extract. External data from an existing map is a promising aid to reach exhaustive and operational results. This involves an additional step of map digitization. But the advantages are worth the effort as the map contains an available complete analysis of the scene, performed during its conception. A selection is then carried out, keeping useful information, smoothing shapes during the generalization step and rejecting details (bushes, hedges, small structures, non-static phenomena). It provides global comprehension of the area in the aerial image, involving network organization, spatial relationships and an exhaustive inventory of meaningful geographical objects.

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Speckle reduction of SAR images using neural networks
Author(s): D. Blacknell, C.J. Oliver, M. Warner
Source: Fifth International Conference on Image Processing and its Applications, 1995, p. 647 - 651
Type: ConferencePaper
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Synthetic aperture radar (SAR) is a high-resolution remote sensing platform with all-weather capability. Traditional filter-based techniques are unsuitable for smoothing SAR images, but considerable success has been achieved using a CPU intensive, algorithmic noise removal process called simulated annealing. In order to reduce the CPU requirements of the despeckling process we have presented a solution based upon neural networks which are a form of adaptive filter. A variety of neural network architectures based on the multilayer perceptron and the vector quantizer network have been trained to learn the despeckling process. We have demonstrated that such a hybrid network can be successfully trained to perform speckle reduction of SAR images. The hybrid network benefits from reduced training and execution times compared to a single MLP, whilst maintaining a good performance.

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Matching SAR images using a hierarchical constraint process
Author(s): A.N. Evans, R.C. Wilson, E.R. Hancock
Source: Fifth International Conference on Image Processing and its Applications, 1995, p. 60 - 64
Type: ConferencePaper
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The authors demonstrate that relaxation labelling methods may be rendered effective in the matching of SAR data, provided that more flexible relational models are adopted. The critical ingredient of their method is the use of a hierarchical graph representation of the scene-data and the model. Here the constraints are graded according to their relational power; the more expressive and potentially more fragile perceptual groupings provide the most compatibility constraints while those with the more ambiguous, yet easily recovered, adjacency relations are much weaker. The application vehicle for the study reported in this paper is the matching of hedges detected in SAR images against their cartographic representation in a digital map. The prerequisites for successful matching are the reliable segmentation of the hedge structures and the recovery of a stable relational description suitable for matching against the map data. The former is achieved via the sequential application of a robust intensity ridge detection algorithm and contour grouping prior to the identification of meaningful linear segments. Since linear hedge structures are largely associated with the quadrilateral boundaries of fields, it is parallel and perpendicular groupings of the segments that potentially provide the most perceptual constraints for use in matching. Unfortunately, under conditions of severe noise and clutter, the reliable identification of such groupings is invariably frustrated.

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Characterisation of forestry species - a comparison using singular value decomposition (SVD) and artificial neural networks (ANN)
Author(s): G.M. Herries, S. Danaher, A. Murray
Source: Fifth International Conference on Image Processing and its Applications, 1995, p. 815 - 819
Type: ConferencePaper
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Singular value decomposition, which has previously been applied to the problem of signal extraction from marine data is currently being implemented for land use classification. Neural networks are becoming increasingly popular for the characterisation of multispectral remote sensing data. There are a number of significant problems with using ANN as classifiers for this type of data. A comparison of these two procedures is performed and there merits and difficulties discussed. The authors introduce the Levenberg Marquardt technique as an advanced method for finding the global minima during a backpropagation training scenario. These techniques are applied to simulated data generated from Landsat TM and SPOT satellite data of the County Wicklow area of Ireland. This data comprises five classes including Sitka spruce and Scots pine.

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