Proceedings of the Symposium on Global and Environmental Monitoring: Proceedings of the Symposium on Global and Environmental Monitoring

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Persistent identifier:: 856665355

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts ; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856665355

Language:: English

Document type:: Multivolume work

Volume

Persistent identifier:: 856669164

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Scope:: XIV, 912 Seiten

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856669164

Illustration:: Illustrationen, Diagramme, Karten

Signature of the source:: ZS 312(28,7,1)

Language:: English

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

Editor:: International Society for Photogrammetry and Remote Sensing, Commission of Photographic and Remote Sensing Data

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:: [FA-1 TOPOGRAPHIC ANALYSIS]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: A Combined Shape-From-Shading and Feature Matching Technique for the Acquisition of Ground Control Points in SAR Images of Rugged Terrain. M. Adair, B. Guindon

Document type:: Multivolume work

Structure type:: Chapter

Each valley segment is traced in turn by searching for logical extensions of the segment in a direction consistent with the current direction of the segment. The tracing stops when there are no more pixels to add to the segment, or when the segment encounters a pixel labelled as part of another valley. In this case, the pixel which has been encountered is labelled as a node. All node locations are stored in a file for the matching process. In the examples presented here, there were 367 nodes and 945 valley segments detected in the SAR mask and 2813 nodes and 4731 valley segments detected in the DEM mask. POINT PATTERN MATCHING The goal of this step is to identify a subset of nodes from each of the masks which correspond to the same location on the ground. The method is loosely based on the relaxation algorithm proposed by Ranade and Rosenfeld (1980). Given a hypothetical transformation between the two masks, we may examine all combinations of point pairs (hereafter a 'point pair' implies one node taken from each image) and determine whether or not the pair 'supports' the hypothesis. The support is calculated by a figure of merit defined by: 4>(x) = (1 +(^ L ) 2 )' 1 where x is the Euclidean distance between the test point pair after applying the transformation. It can be seen that as x increases, the figure of merit decreases. The parameter a is included to allow for an error tolerance in the match. The algorithm used here operates as follows. Every possible combination of point pairs is tested as a hypothetical match with each match defining a hypothetical linear translation between images. An MxN 'support matrix' can thus be defined, where M and N are the number of points in each mask to be matched. The support for each hypothesis is calculated by searching all remaining combinations of point pairs to find an optimum set of pairs, ie. the pairs with the least separation after applying the hypothetical translation. The figure of merit is calculated for each pair in the optimum set and summed. The support matrix is then thresholded at a fixed percentage of the maximum support value in the matrix. The point pairs which give a support value below the threshold are discarded and the calcula tion of the support matrix is repeated using the subset of points which remain after thresholding. The process is iterated until a consistent set of point pairs remains, all of which give support values above the threshold. Since the calculation of the support matrix takes on the order of 0(M 2 N 2 ), the number of points used must be minimized. This is accomplished automati cally by thinning the initial set of points by retaining only those nodes which are connected to long valley segments (since these are most likely to be included in both masks) and by deleting points which are close to one another in the same mask using valley length information stored at the linking and labelling step. Points from each mask are also eliminated at each iteration, and there fore the calculation takes less time as it progresses. The resulting set of point pairs may then be used to define an affine transformation model. The original lists of nodes are searched to find additional point pairs which match within the error tolerance based on this new transformation model. The success of the point pattern matching algorithm depends on having a sufficient number of points in each mask which actually correspond. The low resolution DEM must be sampled at the same resolution as the SAR image resolution to give a similar level of detail in the valley network. Given this condition, the method is robust with respect to the choice of the processing parameters, a and the support threshold. Figure 3 shows the DEM valley mask overlaid on the SAR mask, resampled using the affine transforma tion model which results from the match. In this example, the initial thinning parameters were a minimum valley length of 5 pixels in the SAR mask and 15 pixels in the DEM mask, and a minimum node separation of 5 pixels, leaving approximately 150 nodes from each mask to be used as input to the matching process. A consistent set of 8 point pairs were successfully matched at the end of the iterative process, using a=3 pixels and a threshold of 65%. An additional 51 point pairs were added using the affine transformation model, resulting in an RMS error of approximately 1 pixel (50m) in both the pixel and line directions. CONCLUSIONS A feature-based image matching algorithm for ground control point acquisition has been presented. In the application shown here, valley masks extracted by two different methods have been successfully matched. The algorithm can easily be adapted for other applications where a spatial point pattern of image features may be extracted from two images. The only requirements are for the two images to be roughly similar in scaling and rotation, and have a similar level of detail in the features extracted. REFERENCES Guindon, B., 1989. Development of a Shape-from- Shading Technique for the Extraction of Topographic Models from Individual Spaceborne SAR Images. Proceedings of the 1989 International Geoscience and Remote Sensing Symposium. 597-602. Guindon, B. and H.Maruyama, 1986. Automated Matching of Real and Simulated SAR Imagery as a tool for Ground Control Point Acquisition. Can. J. Remote Sensing. 12:149-158. Qian, J., R.W.Ehrich and J.B.Campbell, 1990. DNESYS - An Expert system for Automatic Extraction of Drainage Networks from Digital Elevation Data. IEEE Trans. Geoscience and Remote Sensing. 28:29- 45. Ranade, S. and A.Rosenfeld, 1980. Point Pattern Matching by Relaxation. Pattern Recognition. 12:269-275. 812

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