Land cover classification for satellite images based on normalization technique and Artificial Neural Network

The Satellite images and the extracted thematic maps provide higher-level information for the recognize, monitoring and management of natural resources. It is very difficult to identify land cover classification manually from a satellite image. The remotely-sensed images are invaluable sources of information for various investigations since they provide spatial and temporal information about the nature of earth surface materials and objects. This study aims to determine the level of contributions of multi-temporal and multi-sensor data together with their principal components for Artificial Neural Network classifiers. The suitability of Back Propagation Neural Network (BPNN) for classification of remote sensing images is explored in this paper. Automatic image classification is one of the challenging problems of recent year. BPN is self-adaptive dynamic system which is widely connected with the large amount of neurons. It can solve the regular problem arise from remote sensing images. This paper discusses about the BPNN method to improve the high resolution remote sensing image. The principle and learning algorithm of BPNN is analyzed and high resolution imagery of Beijing has been used. Back Propagation Neural Network classifies the remote sensing image into the classified image of their pattern recognition.