Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
Simulated annealing: theory and applications
Simulated annealing: theory and applications
Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
A cluster validation index for GK cluster analysis based on relative degree of sharing
Information Sciences—Informatics and Computer Science: An International Journal
Remote Sensing Digital Image Analysis: An Introduction
Remote Sensing Digital Image Analysis: An Introduction
A novel fuzzy classifier based on product aggregation operator
Pattern Recognition
Change Detection of Remote Sensing Images with Semi-supervised Multilayer Perceptron
Fundamenta Informaticae
Robust fuzzy clustering-based image segmentation
Applied Soft Computing
Pattern Recognition, Fourth Edition
Pattern Recognition, Fourth Edition
A novel approach to neuro-fuzzy classification
Neural Networks
International Journal of Approximate Reasoning
A non-local fuzzy segmentation method: Application to brain MRI
Pattern Recognition
Lessons to learn from a mistaken optimization
Pattern Recognition Letters
Semi-supervised change detection using modified self-organizing feature map neural network
Applied Soft Computing
Change detection in remotely sensed images using semi-supervised clustering algorithms
International Journal of Knowledge Engineering and Soft Data Paradigms
Information Sciences: an International Journal
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In this paper we have used two fuzzy clustering algorithms, namely fuzzy c-means (FCM) and Gustafson-Kessel clustering (GKC) along with local information for unsupervised change detection in multitemporal remote sensing images. In conventional FCM and GKC no spatio-contextual information is taken into account and thus the result is not so much robust to small changes. Since the pixels are highly correlated with their neighbors in image space (spatial domain), incorporation of local information enhances the performance of the algorithms. In this work we have introduced a new technique for incorporation of local information. Change detection maps are obtained by separating the pixel-patterns of the difference image into two groups. Hybridization of FCM and GKC with two other optimization techniques, genetic algorithm (GA) and simulated annealing (SA), is made to further enhance the performance. To show the effectiveness of the proposed technique, experiments are conducted on two multispectral and multitemporal remote sensing images. Two fuzzy cluster validity measures (Xie-Beni and fuzzy hypervolume) have been used to quantitatively evaluate the performance. Results are compared with those of existing state of the art Markov random field (MRF) and neural network based algorithms and found to be superior. The proposed technique is less time consuming and unlike MRF does not require any a priori knowledge of distributions of changed and unchanged pixels.