DocumentCode
2509862
Title
Satellite image interpretation using Genetically Optimized Hard C means
Author
Sowmya, B.
Author_Institution
Sathyabama Univ., Chennai, India
fYear
2010
fDate
13-15 Nov. 2010
Firstpage
310
Lastpage
316
Abstract
This paper explains the task of interpreting any given satellite image by Genetically Optimized Hard C means(GOHCM). GOHCM has been used to segment the satellite image. Image segmentation is the process of dividing pixels into homogeneous classes or clusters so that items in the same cluster are as similar as possible and items in different cluster are as dissimilar as possible. The most basic attribute for segmentation is image luminance amplitude for a monochrome image and color components for a color image. Since there are more than 16 million colours available in any given colour image, it is difficult to analyze the image on its entire colour. Hence colour image is converted to gray scale. Genetically Optimized Hard C Means (GOHCM) has been used for segmentation. Depending on the spectral value, the pixels are classified as urban area, bare soil, forest & vegetation and water regions by GOHCM.
Keywords
genetic algorithms; geophysical image processing; image segmentation; unsupervised learning; color image; genetic algorithm; genetically optimized hard C means; image luminance amplitude; monochrome image; satellite image interpretation; satellite image segmentation; unsupervised learning algorithm; Clustering algorithms; Earth; Image color analysis; Image segmentation; Partitioning algorithms; Pixel; Satellites; GOHCM; Genetic Algorithm; HCM; Land Cover; Segmentation;
fLanguage
English
Publisher
ieee
Conference_Titel
Recent Advances in Space Technology Services and Climate Change (RSTSCC), 2010
Conference_Location
Chennai
Print_ISBN
978-1-4244-9184-1
Type
conf
DOI
10.1109/RSTSCC.2010.5712818
Filename
5712818
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