Authors:
G.Karthik,M.Sangeetha,B.Karthik,M.Sriram,DOI NO:
https://doi.org/10.26782/jmcms.spl.2019.08.00042Keywords:
Normalized Difference vegetation index,Geographic information system,Fuzzy C means clustering,Abstract
Geospatial data differ accurately and precisely in the attributes as well as their temporal and spatial dimensions. The two approaches proposed for are road extraction based on Normalized Difference Vegetation Index (NDVI) and Fuzzy c means clustering. Image-based and vector-based algorithms are integrated for conflation. Road Intersections and Terminations of different types of are automatically detected by spatial contextual measure extraction algorithm. Iterative Relaxation Algorithm (IRA) is especially used point matching based at the comparative distance records in among the points. The Vector Road Intersections that is coordinated to removed factor sets by way of a Relaxation-Labeling Algorithm. A Rubber-sheeting Transformation is a neighborhood affined ameliorations, which splits the map parts into small sections and implemented nearby modifications on every piece, also preservative topology in the route. At the end of Rubber-Sheeting Transform there can be misalignment that's befell inside the Road segments. In order to clear up this trouble an energetic Contour Model (snake) that is used to address the outstanding dislocation mistakes. Road network extraction is analyzed and compared based on NDVI and Fuzzy C means clustering .This method can be extended for more information.Refference:
I. Barzohar M. and D. B. Cooper, “Automatic finding of main roads in aerial
images by using geometric-stochastic models and estimation,” IEEE Trans.
Pattern Anal. Mach. Intell., vol. 18, no. 7, pp. 707–721, Jul. 1996.
II. Cobb M. A., M. J. Chung, H. Foley, F. E. Petry, and K. B. Shaw, “A rule-based
approach for the conflation of attributed vector data,” Geoinformatica,vol. 2, no.
1, pp. 7–35, Mar. 1998.
III. Dolphin Kiruba D., Karthik B., Development of period extension and
randomness using RM-PRNG, International Journal of Applied Engineering
Research, V-9, I-22, 6194-6201, 2014
IV. Filin S. and Y. Doytsher. “A Linear Conflation Approach for the Integration
Photogram metric Information and GIS Data”, International archives of
photogrammetry and remote sensing, Volume 33(B3/1): 282-288, 2000.
V. Karthik B., Kiran Kumar T.V.U., Authentication verification and remote digital
signing based on embedded arm (LPC2378) platform, Middle – East Journal of
Scientific Research, V-20, I-12, 2341-2345, 2014
VI. Karthik B., Arulselvi, Selvaraj A., Test data compression architecture for
lowpower vlsi testing, Middle – East Journal of Scientific Research, V-20, I-12,
2331-2334, 2014
VII. Karthik B., Arulselvi, Noise removal using mixtures of projected gaussian scale
mixtures, Middle – East Journal of Scientific Research, V-20, I-12, 2335-2340,
2014
VIII. Karthik B., Kiran Kumar T.V.U., Selvaraj A., Test data compression architecture
for lowpower VLSI testing, World Applied Sciences Journal, V-29, I-8, 1035-
1038, 2014
IX. Karthik B., Kiran Kumar T.V.U., Noise removal using mixtures of projected
gaussian scale mixtures, World Applied Sciences Journal, V-29, I-8, 1039-1045,
2014
X. Karthik B., Kumar T.K., Dorairangaswamy M.A., Logashanmugam E., Removal
of high density salt and pepper noise through modified cascaded filter, Middle –
East Journal of Scientific Research, V-20, I-10, 1222-1228, 2014
XI. Saalfeld A., “Conflation: Automated map compilation,” Int. J. Geograph.
XII. Walter V. and D. Fritsch, “Matching spatial data sets: A statistical approach,”
Int. J. Geograph. Inf. Sci., vol. 13, no. 5, pp. 445–473, Jul. 1999.