Journal
REMOTE SENSING
Volume 6, Issue 4, Pages 2912-2939Publisher
MDPI
DOI: 10.3390/rs6042912
Keywords
machine learning; data mining; urban remote sensing; object-based image analysis
Categories
Funding
- Helmholtz EOS (Earth Observation System)
- EMCA (Earthquake Model Central Asia)
- PROGRESS (Georisiken im Globalen Wandel)
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In this study, a classification and performance evaluation framework for the recognition of urban patterns in medium (Landsat ETM, TM and MSS) and very high resolution (WorldView-2, Quickbird, Ikonos) multi-spectral satellite images is presented. The study aims at exploring the potential of machine learning algorithms in the context of an object-based image analysis and to thoroughly test the algorithm's performance under varying conditions to optimize their usage for urban pattern recognition tasks. Four classification algorithms, Normal Bayes, K Nearest Neighbors, Random Trees and Support Vector Machines, which represent different concepts in machine learning (probabilistic, nearest neighbor, tree-based, function-based), have been selected and implemented on a free and open-source basis. Particular focus is given to assess the generalization ability of machine learning algorithms and the transferability of trained learning machines between different image types and image scenes. Moreover, the influence of the number and choice of training data, the influence of the size and composition of the feature vector and the effect of image segmentation on the classification accuracy is evaluated.
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