Analysis of Satellite Images using Artificial Neural Network
Priyanka Sharma1, Urvashi Mutreja2

1Priyanka Sharma- Assistant Professor in CSE department at Lord Krishna College Of Engineering, Ghaziabad, Uttar Pradesh.
2Urvashi Mutreja- Assistant Professor in CSE department at Lingaya’s University, Faridabad, Uttar Pradesh.
Manuscript received on January 01, 2013. | Revised Manuscript received on January 02, 2013. | Manuscript published on January 05, 2013. | PP: 276-278 | Volume-2, Issue-6, January 2013. | Retrieval Number:  F1178112612/2013©BEIESP
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Abstract: Data from Remote Sensing Satellites are used for various applications of resources survey and management. For collection and analysis of remotely sensed data, Artificial Neural Network(ANN) have become a popular tool. As we know Remotely Sensed images are major sources of data & information which is used in various fields such as Environmental impact analysis, Forest survey, rural to urban change detection(Urban Planning), Mineral Prospecting etc. Although many neural network based Methods has been developed for image classification but some issues still remain to be fixed. Digital interpretation (quantitative analysis) is one of the main approaches for extracting information from remotely sensed image. ‘Classification’ is one of the most common digital technique used as information extraction method from remotely sensed data. In pattern recognition two techniques are used which are supervised classification & unsupervised classification. Supervised Classification is done using Supervised Learning technique according to which the networks know the target and changes accordingly to get the required output corresponding to the input sample data. Already a lot of work has been done in the field of supervised classification. This paper examines remotely sensed data analysis with neural network and unsupervised classification method of ANN for classification of satellite images.
Keywords: ANN, LVQ, Satellite Images, SOFM, SOM.