Taxonomy of nature inspired computational intelligence: A remote sensing perspective

Abstract

The concepts in geospatial sciences are generally vague, ambiguous and imprecise. Also, a combination of spectral, spatial and radiometric resolution of space-borne sensors presents a selective and incomplete look of the geospatial feature/object under its view from the space. Recently, the nature inspired computational intelligence (CI) techniques have emerged as an efficient mechanism to handle diverse uncertainty characteristics. This paper proposes that the human-mind model based computational intelligence techniques, the artificial immune system based computational intelligence techniques; the swarm intelligence based computational intelligence techniques and the emerging geo-sciences based intelligent techniques can be considered as the four pillars of nature inspired CI techniques and hence redefines and extends the taxonomy of nature inspired CI. Researchers have shown keen interest on the applications of natural computing in divergent domains. Scanty references are available on the applications of nature inspired computing in the area of remote sensing. We hence also propose the taxonomy of the most recent nature inspired CI techniques that have been adapted till date for geo-spatial feature extraction and analyze their performances. We also construct a technology timeline of these recent nature inspired CI techniques.