Adopting Semi-supervised Learning Algorithms for Mining Remote Sensing Imagery: Summary of Results and Open Research Problems
نویسندگان
چکیده
We have developed a semi-supervised learning method based on the Expectation-Maximization (EM) algorithm, and maximum likelihood and maximum a posteriori classifiers. This scheme utilizes a small set of labeled and a large number of unlabeled training samples. We have conducted several experiments on multispectral images to understand the impact of unlabeled samples on the classification performance. Our study shows that though in general classification accuracy improves with the addition of unlabeled training samples, it is not guaranteed to get consistently higher accuracies unless sufficient care is exercised when designing a semi-supervised classifier. We also extended this semisupervised framework to model spatial context through Markov Random Fields and initial experiments shows an improved accuracy over MLC, Semi-supervised, and MRF classifiers. Though this study shows that semi-supervised learning schemes can be adopted for remote sensing data mining, there are some open research issues that needs to be solved before these methods can be applied in production environments.
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تاریخ انتشار 2006