Date of Publication :5th July 2017

Abstract: Satellite imaging is being the most attractive source of information for the governmental agencies and the commercial companies in last decade. Satellite images are characterized by weak local correlation between pixels, complete randomness and small multiple regions of interest which makes difficult to segment. The quality of the images is very important especially for the military or the police forces to pick the valuable information from the details. Satellite images may have unwanted signals called as noise in addition to useful information for several reasons such as heat generated electrons, bad sensor, wrong ISO settings, vibration and clouds. There are several image enhancement algorithms to reduce the effects of noise over the image to see the details and gather meaningful information. Satellite images are acquired with remote sensing. Remote sensing is the science and art of obtaining information about an object or area through a device that is not in contact with the object or the area under investigation. The classification can be done by using Image segmentation via various thresholding algorithms where segmentation is the process of dividing an object into several homogeneous regions such that union of no two adjacent regions is homogeneous. In this work an efficient cuckoo search algorithm is developed for segmentation of satellite images.

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