Improving Quantitative Measurements Using Different Segmantation Techniques for Satellite Images
Image segmentation is the most practical approach among all virtually automated image recognition systems. Feature extraction and recognition have numerous applications on telecommunication, weather forecasting, environment exploration and medical diagnosis. This paper deals with different image segmentation algorithms. The quality of satellite image is affected by atmosphere, temperature, etc. By the usage of various segmentation techniques, the image is divided into parts which have strong correlation to reflect the real world body. The different techniques involved in image segmentation are as follows, Edge based image segmentation, Adaptive image thresolding, Watershed segmentation, Region growing segmentation, Quadtree segmentation and Fuzzy c-means segmentation. Using these techniques the quantitative measurements of the satellite images can be improved.