We present a novel method for classification of Synthetic Aperture Radar (SAR) data by
combining ideas from graph-based learning and neural network methods within an active …

Anomaly detection in the hyperspectral images, which aims to separate interesting sparse
anomalies from backgrounds, is a significant topic in remote sensing. In this paper, we …

This paper is a republication of an MMS paper [AL Bertozzi and A. Flenner, Multiscale
Model. Simul., 10 (2012), pp. 1090--1118] describing a new class of algorithms for …

Hyperspectral imagery is a challenging modality due to the dimension of the pixels which
can range from hundreds to over a thousand frequencies depending on the sensor. Most …

We present an accurate and efficient graph-based algorithm for semi-supervised
classification that is motivated by recent successful threshold dynamics approaches and …

Recently, the graphical framework, which provides information about the connections
between pieces of data via a similarity graph, has become a popular setting for problems …

Automatic target recognition with synthetic aperture radar (SAR) data is a challenging
problem due to the complexity of the images and the difficulty in acquiring labels. Recent …

Abstract Machine learning has greatly influenced a variety of fields, including science.
However, despite tremendous accomplishments of machine learning, one of the key …

High-dimensional data classification is a fundamental task in machine learning and imaging
science. In this paper, we propose an efficient and versatile multi-class semi-supervised …

Partitioning a set of graph vertices into two or more subsets constitutes an important class of
problems in combinatorial optimization. Two well-known members of this class are the …