Classification of High-Resolution Remotely Sensed Image by Combining Spectral, Structural and Semantic Features Using SVM Approach

I. Introduction Remote sensing is the acquisition of information about an object or phenomenon without making physical contact with the object. The term generally refers to the use of aerial sensor technologies to detect and classify objects on Earth by means of propagated signals. There are two main types of remote sensing: passive remote sensing and active remote sensing. Passive sensors detect natural radiation that is emitted or reflected by the object or surrounding areas. Active collection, on the other hand, emits energy in order to scan objects and areas where upon a sensor then detects and measures the radiation that is reflected or backscattered from the target. Remote sensing makes it possible to collect data on dangerous or inaccessible areas. Orbital platforms collect and transmit data from different parts of the electromagnetic spectrum, which in conjunction with larger scale aerial or ground-based sensing and analysis, provides researchers with enough information to monitor trends such as El Niño and other natural long and short term phenomena.