Multispectral remote sensing is the collection and analysis of reflected, emitted, or back-scattered energy from an object or an area of interest in multiple bands of regions of the electromagnetic spectrum (Jensen, 2005).
Remote sensing systems gather data via instruments typically carried on satellites in orbit around the Earth.
This energy is recorded as an analog electrical signal and converted into a digital value though an A-to-D conversion.
There are several multispectral remote sensing systems that can be categorized in the following way: A variety of methods can be used for the multispectral classification of images: In this classification method, the identity and location of some of the land-cover types are obtained beforehand from a combination of fieldwork, interpretation of aerial photography, map analysis, and personal experience.
Land-cover refers to the type of material present on the site (e.g. water, crops, forest, wet land, asphalt, and concrete).
Land-use refers to the modifications made by people to the land cover (e.g. agriculture, commerce, settlement).
All classes should be selected and defined carefully to properly classify remotely sensed data into the correct land-use and/or land-cover information.
These differences should be recorded on the imagery and the selection training sites made based on the geographical stratification of this data.
The more common nonparametric algorithms are: Unsupervised classification (also known as clustering) is a method of partitioning remote sensor image data in multispectral feature space and extracting land-cover information.
Using the map, the analyst tries to assign or transform the spectral classes into thematic information of interest (i.e. forest, agriculture, urban).
In the first pass, the program reads through the dataset and sequentially builds clusters (groups of points in spectral space).
The Iterative Self-Organizing Data Analysis Technique (ISODATA) algorithm used for Multispectral pattern recognition was developed by Geoffrey H. Ball and David J.