Applications of Computational Intelligence in Remote Sensing Image Analysis

Unmanned Aerial Systems as a Driving Force in the Digital Information Age

“…Artificial Intelligence technology is not novel, nor new, and extends back decades. In its most simple form, objects or pixels are identified in an image, and an algorithm is used to identify those pixels and objects [9]. Machine learning is a bit more advanced to where those algorithms learn from examples to refine the method.…”

Fuzzy clustering algorithms for unsupervised change detection in remote sensing images

“…Relevance of fuzzy set theoretic methods in pattern recognition and image analysis problems has adequately been addressed in the literature [2,12,15,25,37,45,48,50,53,55,56,63,68]. Fuzzy set theories are reputed to handle uncertainties [42], to a reasonable extent, arising from deficiencies of information available from a situation (the deficiency may result from incomplete, ill-defined, not fully reliable, vague and contradictory information).…”

A Comparative Study of Classical Clustering Method and Cuckoo Search Approach for Satellite Image Clustering: Application to Water Body Extraction

“…By contrast, the unsupervised classification needs fewer data from the analyst; the most necessary one is the image regions' number (Ming-Der 2007). Classification image can be viewed as a clustering problem in the space intensity (Maulik and Bandyopadhyay 2003), and thus several clustering algorithms have been used to resolve it (Tong et al 2009).…”

“…These latter are inspired from the behavior observed in biological systems which learn naturally how to adjust to changes automatically; they are robust, flexible, and evolving (Khalid et al 2011). In remote sensing, BIAs algorithms are generally used as clustering-based methods (Tong, Man, and Xiang 2009). Furthermore, different validity criteria have been developed to evaluate and compare clustering algorithms results.…”