Artificial Neural Network: A Tool for Classification of Land Use and Land Covers Using Satellite Images

An artificial neural network is a system based on the operation of biological neural networks, in other words, is an emulation of biological neural system. Artificial Neural Networks or simply Neural Networks are powerful general purpose computing tools. They have become popular in the analysis of remotely sensed data, particularly in classification or feature extraction from image data more accurately than conventional methods [1]. Knowledge of both land use and land cover is very important for socio-economic and agricultural planning of a region. Land use relates to human activities like residential, institutional, commercial, agricultural, recreational, etc. and the land cover relates to the various types of features on the surface of the earth such as river, canal, forest, agricultural land, bare soil, roads, etc. Remotely sensed image data are often used in land cover and land use applications and classification. Classification is a computational procedure that sorts images into groups (classes) according to their similarities. Training has an important role in Neural Network. Land use/ cover classes that include city, water, soil, forest, various types of agricultural lands, buildings, roads, etc. are clearly classified using Artificial Neural Network (ANN). Recent research has therefore focused on obtaining land use / cover information from high resolution satellite and aerial imagery of land use / cover to extract important features from the large amount of information contained in remote sensing data that requires efficient and intelligent analytical techniques. Image classification and feature extraction are critical in classifying land use /cover from either satellite or aerial imagery. Conventional classification methods cannot recognize the phenomena of some spectrum with different land matters so as to degrade classification accuracy. Neural networks do not require a hypothesis about data distribution; they are valuable tools to classify satellite images [2].