Multiple kernel active learning for robust geo-spatial image analysis

Exploiting disparate features from potentially different data sources with multiple-kernel based machine learning is a promising approach for analyzing geo-spatial data. A mixture-of-kernel approach can facilitate construction of a more effective training data pool with Active Learning (AL). In addition, this could alleviate the computational burden in AL implementations. Kernel based learning requires hyperparameter tuning for model selection. Further, an optimal function is required to integrate different features or data sources appropriately in the kernel induced space. Both kernel parameters and kernel combination functions may need to be tuned at each AL learning step, which is potentially very time-consuming. In this paper, a novel multiple kernel active learning algorithm is proposed that promises enhanced classification, improved AL performance, and a mechanism for automatic selection of kernel weights in the mixture-of-kernels. We demonstrate the usefulness of the proposed framework with results for both feature fusion and sensor fusion tasks.