Abstract
Better interpretation about the contents in high-resolution remote sensing images can be obtained by using multiple features of various types. In order to process large image data sets with high feature dimensions, the very efficient algorithm of kernel extreme learning machine is employed to in our study to build image classifiers. In order to avoid the overflow problem, the classification strategy is improved by training classifiers on different features independently and then fusing the classification results. The effectiveness of the proposed classification approaches are shown by the experimental results achieved on a realistic remote sensing image data set.
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Yao, W., Zeng, Z., Lian, C., Tang, H. (2014). A Kernel ELM Classifier for High-Resolution Remotely Sensed Imagery Based on Multiple Features. In: Zeng, Z., Li, Y., King, I. (eds) Advances in Neural Networks – ISNN 2014. ISNN 2014. Lecture Notes in Computer Science(), vol 8866. Springer, Cham. https://doi.org/10.1007/978-3-319-12436-0_30
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DOI: https://doi.org/10.1007/978-3-319-12436-0_30
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