Image databases are now currently utilized in a wide range of different areas, in particular, the development and application of remote sensing platforms result in the production of huge amounts of image data.
Though advanced image compression technology has solved part of the storage problem, searching and locating through such a database is still a difficult task. In the 90's Content-based Image Retrieval (CBIR) has gained increasing popularity among researchers, however, how to retrieve the content of an image efficiently and effectively still lacks of common recognition. This is because the low level features of an image including color, shape, texture, etc., which could be easily analyzed do not coincide with the high level concepts of an image.
Another major problem in the practical implementation of a CBIR for remotely sensed images is that the content-based indexing and searching process always requires extremely high computational power. On the other hand, the content-based image retrieval algorithms are very suitable for parallel computation as the algorithms can be broken into several data independent processes for running on a parallel computer.
In this paper, we discuss the porting problem of a sequential application of remote sensed image retrieval in a parallel environment using the new paradigm of programming introduced by born of a new structured program languages (Assist 1.2) and evaluate several skeletons composition to optimize the performance of our application.