Comparative Study On Hyperspectral Remote Sensing Images Classification Approaches

Hyperspectral remote sensing image is also known as an “Imaging Spectrometry” emerged as a promising technology for detection and identification of minerals, terrestrial vegetation, man-made materials and backgrounds. The word “Hyperspectral” is used to distinguish sensors with many tens or hundreds of bands from the more traditional multiple sensors. The success of a hyperspectral remote sensing image classification technique depends on many factors. The availability of high-quality remotely sensed imagery and ancillary data, the design of a proper classification procedure, and the analyst’s skills and experiences are the most important ones. For a particular study, it is often difficult to identify the best classifier due to the lack of a guideline for selection and the availability of suitable classification algorithms in hand. Comparative studies of different classifiers are thus frequently conducted. Therefore, in this paper we compared several classification approaches with its factors. Index terms Remote Sensing (RS), Maximum Likelihood Classifier (MLC), Digital Orthophoto Quadrangle (DOQ), Iterative Self-Organizing Data Analysis (ISODATA), Digital Number (DN). ——————————  ——————————