Hybrid Uncertainty-Based Techniques for Segmentation of Satellite Imagery and Applications

Segmentation of an image is an essential assignment in an image examination whereby picture is divided into significant areas whose focuses have almost the same properties like; dim levels, mean qualities, or text-related characteristics. The pictures are divided into locales which best speak to the important articles in the scene. Locale parameters, for example, territory, shape, measurable parameters, and surface can be extricated and utilized for further examination of information. The examination of satellite symbolism of common scenes presents numerous one of a kind issues and it varies from an investigation and division of urban, business, or agrarian ranges. Once the division classes of a picture is obtained, it is conceivable to utilize heuristics or other area particular ways to deal with further characterize, translate, comprehend, register or extract information from the partitioned image. The applications of analysis of satellite imagery is plenty in real-life situations like weather forecasting, analysis of natural scenes, urban planning, environmental monitoring, object recognition, detection of mass wasting, etc. are well known. Several algorithms using classical approaches as well as those using uncertainty-based approaches have been proposed. The analysis shows that hybrid approaches are more efficient than the individual ones. In this chapter, we discuss on all the uncertainty-based and hybrid algorithms for segmentation of satellite imagery and their applications. Also, we propose some open problems which can be handled for future work.