Remote sensing for resources development and environmental management: Remote sensing for resources development and environmental management

Damen, M. C. J.

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Multivolume work

Persistent identifier:: 856342815

Title:: Remote sensing for resources development and environmental management

Sub title:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A. A. Balkema

Identifier (digital):: 856342815

Language:: English

Additional Notes:: Volume 1-3 erschienen von 1986-1988

Editor:: Damen, M. C. J.

Document type:: Multivolume work

Volume

Persistent identifier:: 856343064

Title:: Remote sensing for resources development and environmental management

Sub title:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Scope:: XV, 547 Seiten

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A. A. Balkema

Identifier (digital):: 856343064

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(26,7,1)

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: Damen, M. C. J.

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: 2 Microwave data. Chairman: N. Lannelongue, Liaison: L. Krul

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: Spatial feature extraction from radar imagery. G. Bellavia, J. Elgy

Document type:: Multivolume work

Structure type:: Chapter

100 1) Areas e.g fields, lakes. These are homogeneous features extending over several or more pixels. 2) Boundaries e.g field boundaries. These consist of low-level edge elements where these are defined to be local discontinuities in image features. (Pratt (1978)) . 3) Thin lines e.g rivers, forest rides, roads, hedges. A line segment is defined by the u-shaped cross section of image features. These three types reflect the different approaches to image segmentation. The so called region growing strategy starts by growing an object in an area until a significant change in the measured features is noted. Another segmentation strategy aims to locate object boundaries thereby isolating the objects. The overall feature extraction process may consist of one or more of the following classes of operations. 1) Preprocessing: This is primarily to remove the effect of image noise on the following stages. 2) Feature Extraction: The actual process of transforming the image to a specific feature domain. 3) Post Processing: The process by which detected features are cleaned i.e filtered of invalid or unwanted results. Knowledge of the features to be extracted can be used at any of the three stages. For example knowledge of the noise statistics (embodied into a noise model) may be used to optimally reduce noise in stage 1. In stage 2 a knowledge of the feature type may be used to reduce the computational cost. World knowledge can also be used in stage 3 to test the validity of detected features. 2 EDGE AND LINE FEATURE EXTRACTION Extensive surveys of the following classes of techniques can be found in Ballard(1982), Pratt(1978), Davis(1975), Carlotto(1984) , Duda & Hart(1972b). 2.1 Local Operators Local operators use data from the neighbourhood of the edge candidate pixel. There are a wide variety of such local operators details of which can be found in Duda & Hart(1972b) and Pratt (1978). These simple operators often form the basis of commercial edge detection systems because of their computational simplicity and their potential parallel implementation. More sophisticated statistical operators have attempted to increase the detection performance in the presence of noise however Geiss(1984) found the Marr and Hildreth (1980) operator unsuccessful at locating edges in Synthetic Aperture Radar images and although the technique developed by Suk and Hong(1984) proved superior to simple operators it still, performed unacceptably in radar data. 2.2 Image Modelling The following set of techniques all attempt to impart some level of knowledge into the feature extraction process. 2.2.1 Parametric Modelling The basis of parametric modelling is to fit some parametric surface representing an ideal edge model to a local region of pixels from which the edge characteristics can then be derived. Again many researchers have proposed operators in this category which can be found in Rosenfeld and Kak(1982), Haralick (1980) and Chittineni(1983) . transform is the F high pas produces produced transform Unfortuns processe: structure 2.2.2 Statistical Modelling 2.4 Graph Statistical modelling forms the basis of a variety of methods which rely on statistical theory for edge detection and involves the combination of a noise and image model allowing calculation of probabilities of edge existence. Then statistical methods such as maximum likelihood estimation can be used to locate probable edges. Chen and Pavlidis(1980). Rosenfeld(1981). 2.2.3 Template Matching In this method explicit knowledge of the desired feature shape is used to produce a template which is then matched to regions of the image. The feature will probably exist where the correlation between template and image data is greater than a set threshold. The method is successful for detecting very specific objects such as tanks and aircraft in military images where the template may be a section of an image containing the desired object. Simple template operators such as the Kirsch operator in Pratt(1978) attempt to generalise the process by detecting line and edge elements with variously orientated templates. Another method related to template matching by Stockman and Agrawala (1977) is the Hough Transform technique (Hough (1962)) later improved by Duda & Hart(1972a). For grey level images the transform produces peaks and troughs in Hough Space corresponding to light and dark straight lines in image space. These peaks or troughs can then be detected and the line parameters extracted, using traditional operators. In general the transform can be used to extract any feature shape which has a known parametric form such as the conic sections (Sloan & Ballard(1980)) and has been widely used because of its effectiveness in both clean and noisy images (see Shapiro(1978)). For radar images the technique performs successfully at detecting straight lines that are long relative to the scene size (Skingley & Rye(1985)). More recently a modified hough transform, the MUFF transform (Wallace(1985)) has been developed which uses a different parametric form for lines to enable the detection of short lines. The radon transform shown to be equivalent to the hough method by Deans (1981) is an invertible transform and so has been used for linear feature enhancement as shown by Murthy(1985) for radar images and for detection of straight lines in simulated noisy imagery as illustrated by Nasrabadi(1984). 2.3 Transform Domain Processing This class of processing technique comprises a global two dimensional weighted transform followed by a spatial frequency filter in the A graph consists between r arc is a then seer path betw' graph. IJ known the solution demon st rai solution (Palay(19 the usefu for nois guide the 3 discuss: Local ope: are typi calculate approach is thus si noise. Ot susceptib: with edge effects sophistic, in low si modelling but param* disadvantc modelling accurate : they work known noi; algorithm specific i being lo < embody mo fully gi techniques and do nc noise. Gr< global or evaluation to noise, optimal Usually tl processes of knowle extensive exponenti practical initial b provided. (1984) has this class efficiency We may n thin line eharacteri human visu criteria. 1) Globa!

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