Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Pattern recognition using neural networks: theory and algorithms for engineers and scientists
Pattern recognition using neural networks: theory and algorithms for engineers and scientists
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Clustering Algorithms
Extraction of Shift Invariant Wavelet Features for Classification of Images with Different Sizes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture features for DCT-coded image retrieval and classification
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
Cluster Validity for the Fuzzy c-Means Clustering Algorithrm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classification of Satellite Images Using Partitioned-Feature Based Classifier Model
ICISA '11 Proceedings of the 2011 International Conference on Information Science and Applications
Classification of MPEG VBR video data using gradient-based FCM with divergence measure
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
Classification of audio signals using gradient-based fuzzy c-means algorithm with divergence measure
PCM'05 Proceedings of the 6th Pacific-Rim conference on Advances in Multimedia Information Processing - Volume Part I
A Convergence Theorem for the Fuzzy ISODATA Clustering Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Satellite image classification using a classifier integration model
AICCSA '11 Proceedings of the 2011 9th IEEE/ACS International Conference on Computer Systems and Applications
Image data classification using fuzzy c-means algorithm with different distance measures
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
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A satellite image classifier scheme by using a Fuzzy c-Means (FcM) algorithm is proposed in this paper. The FcM algorithm adopted in this paper is a Gradient-based FcM with Divergence measure (GFcM(D)) and it utilizes the Divergence measure to exploit the statistical nature of the image data and thereby improves the classification accuracy. Experiments and results on a set of satellite images demonstrate that the proposed GFcM(D)-based classifier outperforms conventional algorithms such as the traditional Self-Organizing Map (SOM) and Fuzzy c-Means (FcM) in terms of classification accuracy.