Dot Diffusion Block Truncation Coding for Satellite Image Retrieval

Abstract

In present survey it is noticed that the profound interest in research and study of retrieval of satellite images and Image Retrieval on Content Based is grown hugely .Thus, to build the semantic error which is a huge challenge. Also, it prevents wide hosting of image on content based search engines which is now the necessity of CBIR technique. Mostly image search engine depend on human generated data as input query namely tags or annotations. The images get stored in database according to the annotations or tags assigned to them. Thus, tags or annotations are used as inputs by search engines .Thus, to overcome the limitation of annotations based image retrieval an Improved Block Truncation Coding is used which is based on Texture and color features of an image. In this paper, Improved Block Truncation Coding technique is used to fill the semantic gap. Then the similarity features type of search is carried to find different query regions namely desert, coastal, forest, metro from the satellite images. The retrieval technique can be practically applied and compared using different similarity methods .The technique of Improved Block Truncation Coding is described which is one of the most proficient methods used in retrieval of satellite images. Though, in traditional BTC certain redundancy like false contour, inherent artifacts are observed. In order to deal with this an improved BTC with dotdiffusion is applied to the system. Here, a Dot Diffusion is added for retrieving the most relevant images that best match to the query image whereas Dot Diffusion is used to give best quality image with good clearity and save memory need to store in database.