2 research outputs found
Combining multiple resolutions into hierarchical representations for kernel-based image classification
Geographic object-based image analysis (GEOBIA) framework has gained
increasing interest recently. Following this popular paradigm, we propose a
novel multiscale classification approach operating on a hierarchical image
representation built from two images at different resolutions. They capture the
same scene with different sensors and are naturally fused together through the
hierarchical representation, where coarser levels are built from a Low Spatial
Resolution (LSR) or Medium Spatial Resolution (MSR) image while finer levels
are generated from a High Spatial Resolution (HSR) or Very High Spatial
Resolution (VHSR) image. Such a representation allows one to benefit from the
context information thanks to the coarser levels, and subregions spatial
arrangement information thanks to the finer levels. Two dedicated structured
kernels are then used to perform machine learning directly on the constructed
hierarchical representation. This strategy overcomes the limits of conventional
GEOBIA classification procedures that can handle only one or very few
pre-selected scales. Experiments run on an urban classification task show that
the proposed approach can highly improve the classification accuracy w.r.t.
conventional approaches working on a single scale.Comment: International Conference on Geographic Object-Based Image Analysis
(GEOBIA 2016), University of Twente in Enschede, The Netherland