Modeling and Optimization of Signals Using Machine Learning Techniques

Abstract
All bodies, planets, living beings, and inanimate objects emit electromagnetic
radiation, and the amount and type of radiation emitted depend largely on their
temperatures. Electromagnetic radiation may be emitted by an object or may
come from another body and could be reflected by it. There are operational sat-
ellite systems that sample virtually every region of the electromagnetic spectrum,
with spatial resolutions from 0.5 to 5,000 m. The scientific community’s interest in
spatiotemporal studies of global change, environmental monitoring, and human
impacts involves the use of remote sensing data. Remote sensing systems, par-
ticularly those located on satellites, provide a repetitive and synoptic vision of
Earth, which is of great interest in monitoring and analyzing human activities and
their impacts. The activities include the evaluation and monitoring of the envi-
ronment like urban growth and hazardous waste; detection and monitoring of
global changes, deforestation, global warming, and exploration of non-renewable
resources and their land use; civil engineering;, the acquisition of satellite imag-
ery from relevant sources; the preprocessing of satellite imagery as per require-
ment; and the development and classification of satellite data using fuzzy C-means
(FCM), modified FCM, K-means, and fuzzy inference system (FIS) techniques.
Keywords:  FCM, modified FCM, K-means, FIS techniques