Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Introductory Digital Image Processing: A Remote Sensing Perspective
Introductory Digital Image Processing: A Remote Sensing Perspective
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Combining Pattern Classifiers: Methods and Algorithms
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Tri-Training: Exploiting Unlabeled Data Using Three Classifiers
IEEE Transactions on Knowledge and Data Engineering
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
A RBF classifier with supervised center selection and weighted norm
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Greedy regression ensemble selection: Theory and an application to water quality prediction
Information Sciences: an International Journal
Change Detection of Remote Sensing Images with Semi-supervised Multilayer Perceptron
Fundamenta Informaticae
International Journal of Approximate Reasoning
Information Sciences: an International Journal
When Semi-supervised Learning Meets Ensemble Learning
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Aggregation Pheromone Density Based Pattern Classification
Fundamenta Informaticae
Image Analysis, Classification, and Change Detection in Remote Sensing: With Algorithms for ENVI/IDL, Second Edition
Semi-Supervised Learning
Fuzzy clustering algorithms for unsupervised change detection in remote sensing images
Information Sciences: an International Journal
Ensemble of feature sets and classification algorithms for sentiment classification
Information Sciences: an International Journal
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
A new approach for time series prediction using ensembles of ANFIS models
Expert Systems with Applications: An International Journal
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Semi-supervised multiple classifier systems: background and research directions
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Neural Networks
A Hopfield Neural Network for Image Change Detection
IEEE Transactions on Neural Networks
A Kernel Approach for Semisupervised Metric Learning
IEEE Transactions on Neural Networks
Semisupervised Learning Using Negative Labels
IEEE Transactions on Neural Networks
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In this article, a novel approach using ensemble of semi-supervised classifiers is proposed for change detection in remotely sensed images. Unlike the other traditional methodologies for detection of changes in land-cover, the present work uses a multiple classifier system in semi-supervised (leaning) framework instead of using a single weak classifier. Iterative learning of base classifiers is continued using the selected unlabeled patterns along with a few labeled patterns. Ensemble agreement is utilized for choosing the unlabeled patterns for the next training step. Finally, each of the unlabeled patterns is assigned to a specific class by fusing the outcome of base classifiers using some combination rule. For the present investigation, multilayer perceptron (MLP), elliptical basis function neural network (EBFNN) and fuzzy k-nearest neighbor (k-nn) techniques are used as base classifiers. Experiments are carried out on multi-temporal and multi-spectral images and the results are compared with the change detection techniques using MLP, EBFNN, fuzzy k-nn, unsupervised modified self-organizing feature map and semi-supervised MLP. Results show that the proposed work has an edge over the other state-of-the-art techniques for change detection.