Title: An
efficient computational framework for labeling large scale
spatiotemporal remote sensing datasets
Abstract:
In this talk, We present a novel framework for semi-supervised
labeling of regions in remote sensing image datasets. Our
approach works by decomposing the image into irregular patches
or superpixels and derives novel features based on intensity
histograms, geometry, corner density, and scale of tessellation.
Our classification pipeline uses either k-nearest neighbors
or SVM to obtain a preliminary classification which is then
refined using Laplacian propagation algorithm. Our approach
is easily parallelizable and fast despite the high volume
of data involved. Results are presented which showcase the
accuracy as well as different stages of our pipeline.
https://www.cise.ufl.edu/people/faculty/ranka/
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