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Very High Resolution Optical Image Classification Using Watershed Segmentation and a Region-Based Kernel | IEEE Conference Publication | IEEE Xplore

Very High Resolution Optical Image Classification Using Watershed Segmentation and a Region-Based Kernel


Abstract:

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 m...Show More

Abstract:

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.
Date of Conference: 22-27 July 2018
Date Added to IEEE Xplore: 04 November 2018
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Conference Location: Valencia, Spain

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