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

101 ng is to fit :ing an ideal pixels from can then be have proposed can be found iralick (1980) ; basis of a a statistical involves the image model ities of edge hods such as in be used to Chen and edge of the to produce a to regions of obably exist template and et threshold, ¡tecting very and aircraft nplate may be j the desired such as the attempt to :ing line and ' orientated late matching is the Hough 962)) later i) . For grey ces peaks and ding to light space. These ected and the g traditional sform can be e which has a s the conic and has been sctiveness in mages (see images the at detecting lative to the L985)) . More orm, the MUFF een developed :ric form for short lines, be equivalent (1981) is an been used for as shown by fes and for in simulated strated by que comprises ted transform filter in the transform domain. The most common transform is the Fourier which when combined with a high pass filter and thresholding process produces an edge map equivalent to that produced by local operators. Various transforms and filtering strategies exist. Unfortunately even the most sophisticated processes fail at detecting thin line structures in noisy environments. 2.4 Graph Search Methods A graph is a mathematical object that consists of a set of nodes {n^} and arcs between nodes <n^,nj>. Associated with each arc is a cost . The edge or line search is then seen as a search for the minimum-cost path between two nodes of a suitably weighted graph. If some measure of a best line is known then the search may be optimal and the solution found by dynamic programming as demonstrated by Montanari(1971) otherwise the solution may only be satisficing (Palay(1985)) . Martelli (1972) demonstrated the usefullness of this class of technique for noisy images by using heuristics to guide the search. 3 DISCUSSION Local operators as a method of edge detection are typically deterministic and aim to calculate the local gradient image. This approach involves difference operations and is thus susceptible to high spatial frequency noise. Other operators aim to reduce this susceptibility by combining noise suppression with edge detection but with derogatory effects on thin lines and the most sophisticated techniques prove unsuccessful in low signal to noise environments. Image modelling techniques offer some improvement but parametric modelling exhibits many of the disadvantages of local operators. Statistical modelling methods can be global but need accurate image and noise models. Consequently they work well for synthesised images with known noise distributions. Template matching algorithms are excellent for extracting specific feature shapes but still suffer from being local in nature. Hough transforms embody more global concepts but cannot be fully generalised. Spatial frequency techniques are insensitive to fine structures and do not distinguish between signal and noise. Graph search strategies can have both global or local predicates built into an evaluation function and so may be made robust to noise. They are flexible and can provide optimal or good satisficing solutions. Usually they embody both serial and parallel processes and thus may incorporate any level of knowledge. Their disadvantage is the extensive computation involved which may grow exponentially with scene size so that practical applications usually require an initial boundary estimate to be manually provided. However Bertolazzi and Pirozzi (1984) has developed a parallel algorithm for this class of problem offering much improved sfficiency. We may now think of the ideal technique for thin line feature extraction and its characteristics. For this one looks to the human visual system to suggest the following criteria. 1) Global predicates must be used. 2) The probability of an edge or line existing at a certain pixel location is dependent on other possible edges in the scene. 3) Noise models should not be needed. 4) Only simple models of features should be used. 5) The technique should allow for generalisation. 6) Computational effort involved should be related to the signal to noise level. The published research indicates that thé graph searching approach to thin line detection is the most appropiate for noisy scenes. The important problem that remains however is how to devise a suitable evaluation function which will encompass the criteria listed above. Various evaluation functions are currently being devised and studied. This is an important continuing part of the research. The sophistication of this type of approach implies that very intensive computation is required but it is felt that recent advances in computer technology such as parallel processors & transputers render such concerns irrelevant . It is more important that the problems of feature extraction be tackled, rather than the specific dificulties of implementation on current computers. 4 CONCLUDING REMARKS The major classes of thin line feature extraction techniques have been reviewed with emphasis placed on their suitability for line extraction in the presence of image noise. The decision has been taken to pursue the graph search strategy and to develop and test a generalised algorithm. Finally a successful line feature algorithm may be synergistically combined with an area based segmentation technique to produce the mythical perfect image segmentation. Such a technique may then be easily integrated into an automatic interpretation schema. REFERENCES Ballard, D.H. & Brown, C.M. 1982. Computer Vision. Bertolazzi, P. & Pirozzi, M. 1984. A Parallel Algorithm for the Optimal Detection of a Noisy Curve. Computer Vision, Graphics & Image Processing. 27:380-386. Carlotto, M.J etal. 1984. Feature Extraction Assesment Study. Report no. ETL 0377 DACA76 82C 0004. Chen, P.C. & Pavlidis, T. 1980. Image Segmentation as an Estimation Problem. In Rosenfeld(1981). Chittineni, C.B. 1983. Edge and Line Detection in Multidimensional Noisy Imagery Data. IEEE Transactions on Geoscience and Remote Sensing. 21,2:163-174. Davis, L.S. 1975. A Survey of Edge Detection Techniques. Computer Graphics and Image Processing. 4:248-270. Deans, S.R. 1981. Hough Transform from the Radon Transform. IEEE Transactions on PAMI.3,2:185-188. Duda, R.O. & Hart, P.E. 1972a. Use of the Hough Transformation to Detect lines and Curves in Pictures. Communications of the ACM. 15,1:11-15. Duda, R.O & Hart, P.E. 1972b. Pattern Classification And Scene Analysis. Wiley Interscience.

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