An efficient computational framework for labeling large scale spatiotemporal remote sensing datasets

Abstract

We present a novel framework for semisupervised 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.