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Multisource Composite Kernels for Urban-Image Classification
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DOI：10.1109/LGRS.2009.2015341.png)
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Abstract
En 中文
This letter presents advanced classification methods for very high resolution images. Efficient multisource information, both spectral and spatial, is exploited through the use of composite kernels in support vector machines. Weighted summations of kernels accounting for separate sources of spectral and spatial information are analyzed and compared to classical approaches such as pure spectral classification or stacked approaches using all the features in a single vector. Model selection problems are addressed, as well as the importance of the different kernels in the weighted summation.
Keywords:
Multiple kernel learning
support vector machines (SVMs)
urban monitoring
very high resolution image
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