Improved remote sensing image segmentation using fuzzy logic for dynamic statistical region merging algorithm

Remote sensors gather information by measuring the electromagnetic radiation that's reflected, emitted and absorbed by objects in various spectral regions, from gamma-rays to radio waves. To measure this radiation, both active and passive remote sensors are utilized. Image segmentation may be the division of a picture into regions or categories, which correspond to different objects or areas of objects. Image segmentation is a significant tool in image processing and can serve becoming an efficient front end to sophisticated algorithms and thereby simplify subsequent processing. It is the procedure of dividing an image into homogenous and non-overlapping regions, that is a significant step toward higher level image processing such as for instance for example image analysis, pattern recognition and automatic image interpretation. This paper is targeted on remote sensing image segmentation for dynamic statistical region merging algorithm using fuzzy logic. To improve the correctness of the remote sensing images dynamic statistical region merging-automatic scale value is proposed to acquire the automatic scale values. The remote sensing images were employed for experimental purpose. Quantitative analysis was performed on the cornerstone of geometric accuracy, specificity and sensitivity. This method can consistently segment remote sensing images with high efficiency.