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.

Graph-based semi-supervised classification on very high resolution remote sensing images

“…In this paper, we extend the work of [33,34] by employing a decision tree to generate semantic binary features, and combine the SVM hinge loss function with a graph Laplacian based regularization in order to implement the semi-supervised classification. Our combined approach-henceforth, referred as GLSVM-is better capable of exploiting the semantic correlation of the inter-class and intra-class feature attributes and also significantly reduce the complexity of the pipeline framework presented in [33,34]. We will describe the details in the following sections.…”