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Improving the Accuracy of Object Based Supervised Image Classification using Cloud Basis Functions Neural Network for High Resolution Satellite Images
Imdad Ali Rizvi, B.Krishna Mohan
Pages - 342 - 353     |    Revised - 30-08-2010     |    Published - 30-10-2010
Volume - 4   Issue - 4    |    Publication Date - October 2010  Table of Contents
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KEYWORDS
Accuracy assessment, Object based image classification, Radial basis functions neural network.
ABSTRACT
A lot of research has been undertaken and is being carried out for developing an accurate classifier for extraction of objects with varying success rates. Most of the commonly used advanced classifiers are based on neural network or support vector machines, which uses radial basis functions, for defining the boundaries of the classes. The drawback of such classifiers is that the boundaries of the classes as taken according to radial basis function which are spherical while the same is not true for majority of the real data. The boundaries of the classes vary in shape, thus leading to poor accuracy. This paper deals with use of new basis functions, called cloud basis functions (CBFs) neural network which uses a different feature weighting, derived to emphasize features relevant to class discrimination, for improving classification accuracy. Multi layer feed forward and radial basis functions (RBFs) neural network are also implemented for accuracy comparison sake. It is found that the CBFs NN has demonstrated superior performance compared to other activation functions and it gives approximately 3% more accuracy.
CITED BY (11)  
1 Akar, A., Gökalp, E., Akar, Ö., & Yilmaz, V. (2016). Improving classification accuracy of spectrally similar land covers in the rangeland and plateau areas with a combination of WorldView-2 and UAV images. Geocarto International, (just-accepted), 1-26.Yadav, S., Rizvi, I., & Kadam, S. Comparative study of object based image analysis on high resolution s
2 Rizvi, I. A., & Kadam, M. M. (2015). Proposed Algorithm for Shadow Identification and Classification in VHR Satellite Imagery. Journal of Remote Sensing & GIS, 6(3), 33-44.
3 Yadav, S., Rizvi, I., & Kadam, S. Urban Tree Canopy Detection Using Object-Based Image Analysis for Very High Resolution Satellite Images: A Literature Review.
4 Paviour, S. J. (2014). Carbon sequestration and trading potential in semi-arid South Africa: a Karoo case study (Doctoral dissertation, Stellenbosch: Stellenbosch University).
5 Panchal, A. J., Rizvi, I. A., & Kadam, m. m. shadow detection and classification from very high resolution satellite images using support vector machine.
6 Madasamy, B., & Tamilselvi, J. J. Improving classification Accuracy of Neural Network through Clustering Algorithms.
7 Prasad, D. K. (2012). Survey of the problem of object detection in real images. International Journal of Image Processing (IJIP), 6(6), 441.
8 Rizvi, I. A., & Mohan, B. K. (2011). Object-based image analysis of high-resolution satellite images using modified cloud basis function neural network and probabilistic relaxation labeling process. Geoscience and Remote Sensing, IEEE Transactions on, 49(12), 4815-4820.
9 Rizvi, I., Mohan, B. K., & Narayana, E. L. (2011). accuracy enhancement of object based image classification using relaxation labeling process for high resolution satellite images. In ASPRS 2011 Annual Conference (pp. 1-8).
10 Rizvi, I., Mohan, B. K., & Narayana, E. L. (2011). Accuracy Enhancement of Object Based Image Classification Using Relaxation Labeling Process for High Resolution Satellite Images. In ASPRS 2011 Annual Conference (pp. 1-8).
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Mr. Imdad Ali Rizvi
Indian Institute of Technology Bombay - India
imdadrizvi@iitb.ac.in
Associate Professor B.Krishna Mohan
Indian Institute of Technology Bombay, - India