Automatic Generation of Standard Deviation Attribute Profiles for Spectral–Spatial Classification of Remote Sensing Data
Journal Title: INTERNATIONAL JOURNAL OF COMPUTER TRENDS & TECHNOLOGY - Year 2013, Vol 5, Issue 5
Abstract
In recent years, the development of high-resolution remote sensing image extends the visual field of the terrain features. Back Propagation neural network is widely used in remote sensing image classification in recent years, it is a self-adaptive dynamical system which is widely connected by large amount of neural units, and it bases on distributing store and parallel processing. It study by exercise and had the capacity of integrating the information, synthesis reasoning, and rapid overall processing capacity. It can solve the regular problem arise from remote sensing image processing; therefore, it is widely used in the application of remote sensing. Utilizing data mining tasks such as classification on spatial data is more complex than those on non-spatial data. It is because spatial data mining algorithms have to consider not only objects of interest itself but also neighbors of the objects in order to extract useful and interesting patterns. One of classification algorithms namely the ID3 algorithm which originally designed for a non-spatial dataset has been improved by other researchers in the previous work to construct a spatial decision tree from a spatial dataset containing polygon features only. The objective of this paper is to propose a new spatial decision tree algorithm based on the ID3 algorithm for discrete features represented in points, lines and polygons. As in the Improved ID3 algorithm that use information gain in the attribute selection, the proposed algorithm uses the spatial information gain to choose the best splitting layer from a set of explanatory layers. The new formula for spatial information gain is proposed using spatial measures for point, line and polygon features. In this proposed work, the use of Back Propagation neural network classifier and decision tree approach will be implemented on the high resolution remote sensing images of their pattern recognition Proposed system will shows better performance compare to existing RF and SVM based classification techniques.
Authors and Affiliations
Ch. Bhavani , Y. Jaya babu
Keywords
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