Title :
Satellite image interpretation using Genetically Optimized Hard C means
Author_Institution :
Sathyabama Univ., Chennai, India
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;
Conference_Titel :
Recent Advances in Space Technology Services and Climate Change (RSTSCC), 2010
Conference_Location :
Chennai
Print_ISBN :
978-1-4244-9184-1
DOI :
10.1109/RSTSCC.2010.5712818