Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects

baltsavias, emmanuel p.

Bibliographic data

Monograph

Persistent identifier:: 856473650

Author:: Baltsavias, Emmanuel P.

Title:: Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects

Sub title:: Joint ISPRS/EARSeL Workshop ; 3 - 4 June 1999, Valladolid, Spain

Scope:: III, 209 Seiten

Year of publication:: 1999

Place of publication:: Coventry

Publisher of the original:: RICS Books

Identifier (digital):: 856473650

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

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

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:: Monograph

Collection:: Earth sciences

Chapter

Title:: TECHNICAL SESSION 2 PREREQUISITES FOR FUSION / INTEGRATION: IMAGE TO IMAGE / MAP REGISTRATION

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: AUTOMATED PROCEDURES FOR MULTISENSOR REGISTRATION AND ORTHORECTIFICATION OF SATELLITE IMAGES. Ian Dowman and Paul Dare

Document type:: Monograph

Structure type:: Chapter

International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999 42 Figure 7. Flow-chart summarising patch matching procedure. The cost function used by Morgado and Dowman (1997) is: T = ^\a l - a 2 \+\p i -p 2 \ + \r l - r 2 \+\c l -c 2 \+... where T is the value of the cost function, a/ is the area of patch i, Pi is the perimeter length, and rj and q are the length and width of the bounding rectangle. A specific problem associated with this function is that it was found that the area component influenced the results more than the other attributes. To get around this problem, the value of the area component was halved to reduce its influence. However, since areas and lengths are being compared, it seems more reasonable to take the square root of the area component, so that all the components being compared have the same dimensionality. Furthermore, with the above function, larger patches will always produce a larger value than smaller patches. Therefore, to ensure this is not the case, the differences in components have been normalised with respect to patch size. Thus, the cost function used in this study is expressed by the equation: l a \ ~ a 2 2 + Pi -Pi + r i ~ r 2 + C i C 1 «1 +a 2 Pl+ Pi r i +r i Cj + c 2 are shown in Table 2. It is apparent from this table that by using a combination of the cost function, the patch separation and the overlap, correct matches can be identified. Although the cost function is not by itself reliable, there is a clear bimodal distribution of the patch separations and the overlap. Using this method, correct matches can be identified and these are shown in Figure 8. In order to determine the best method of segmentation, the tests were repeated for all methods of patch extraction using the Istres images and another pair. The results do not allow any conclusions about the best method, only that there are large variations with image and method. For both these images it is interesting that the number of correct matches from all combinations varied from 0 to 5 out of total matches varying from 39 to 366. The correct matches were successfully used to register the SPOT and SAR images together. SAR patch index SPOT patch index Cost function Patch separation (pixels) Percentage overlap (x 100) 2 5204 0.463 112.429 0.194 3 0,484 0.353 0.885 7 13897 0.270 225.892 0.331 9 3417 0.550 253.794 0.312 10 2901 ¡1 0.277 4.651 0.899 12 3754 0.229 6.835 0.944 13 18389 0.265 267.599 0.199 18 26749 0.240 514.928 0.372 21 13746 0.662 209.264 0.668 22 21272 0.394 275.525 0.366 24 8585 0.259 378.102 0.401 28 18558 0.306 166.298 0.493 30 21476 0.327 198.795 0.526 32 11307 0.313 59.894 0.561 33 25114 0.129 242.566 0.561 37 21579 0.137 303.653 0.652 39 20029 0.286 395.225 0.573 43 16449 0.549 43.834 0.307 45 15793 0.287 3.995 0.887 56 1339 0.414 330.546 0.230 60 11922 0.416 382.007 0.629 61 16119 0.474 123.264 0.596 71 17966 0.215 347.164 0.514 73 13154 0.714 222.182 0.290 75 6637 0.276 384.673 0.482 76 16520 0.186 386.544 0.351 77 4572 0.382 418.060 0.471 Table 2. Matching results with correct matches highlighted. 5. EDGE MATCHING Every patch in image 2 is matched with each patch in image 1 and the matches with the lowest cost function are retained. There will be false and multiple matches and these are reduced by eliminating matches with large differences in centroid values. A further technique used to improve matching is to repeat the operation with image 2 as the master and image 1 as the slave. To compare shape is a further check. Patches are compared by counting the number of pixels in common when centroids are made coincident. Those with large overlap are considered to be the best matches. The results of these processes The polygon matching provides directly the centroid co ordinates of conjugate polygons and these can be used to carry out a transformation between the two images. This transformation can be improved by matching the detail of the edges around the polygons. The basic method of edge matching using dynamic programming is described by Newton et al. (1994). This previous work used the edges of the polygons as extracted by the segmentation. In the new method, edges are extracted from the raw data in the region of the polygon boundary and then matched using dynamic programming as before. In this way, it is ensured that reliable edges are extracted

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