Very High Resolution Optical Image Classification Using Watershed Segmentation And A Region-Based Kernel

In this paper, the problem of the spatial-spectral classification of very high-resolution optical images is addressed using a kernel-and region-based approach. A novel method based on integrating region-based or object-based information into a kernel machine is developed. A Gaussian process model is used to characterize each segment in a segmentation map and to define a region-based admissible kernel accordingly. This kernel is combined with a marker-controlled watershed segmentation that incorporates scale adaptivity. Spatial-spectral fusion capabilities are also ensured by combining the resulting classification method with composite kernels.