Multimodal hyperspectral remote sensing: An overview and perspective
Since the advent of hyperspectral remote sensing in the 1980s, it has made important
achievements in aerospace and aviation field and been applied in many fields …
achievements in aerospace and aviation field and been applied in many fields …
Batch active learning for multispectral and hyperspectral image segmentation using similarity graphs
Graph learning, when used as a semi-supervised learning (SSL) method, performs well for
classification tasks with a low label rate. We provide a graph-based batch active learning …
classification tasks with a low label rate. We provide a graph-based batch active learning …
Uncertainty quantification in graph-based classification of high dimensional data
Classification of high dimensional data finds wide-ranging applications. In many of these
applications equipping the resulting classification with a measure of uncertainty may be as …
applications equipping the resulting classification with a measure of uncertainty may be as …
Blind hyperspectral unmixing based on graph total variation regularization
Remote sensing data from hyperspectral cameras suffer from limited spatial resolution, in
which a single pixel of a hyperspectral image may contain information from several materials …
which a single pixel of a hyperspectral image may contain information from several materials …
Two-dimensional compact variational mode decomposition: spatially compact and spectrally sparse image decomposition and segmentation
Decomposing multidimensional signals, such as images, into spatially compact, potentially
overlapping modes of essentially wavelike nature makes these components accessible for …
overlapping modes of essentially wavelike nature makes these components accessible for …
Dynamical spectral unmixing of multitemporal hyperspectral images
In this paper, we consider the problem of unmixing a time series of hyperspectral images.
We propose a dynamical model based on linear mixing processes at each time instant. The …
We propose a dynamical model based on linear mixing processes at each time instant. The …
Diffuse interface models on graphs for classification of high dimensional data
AL Bertozzi, A Flenner - siam REVIEW, 2016 - SIAM
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 …
Model. Simul., 10 (2012), pp. 1090--1118] describing a new class of algorithms for …
A graph-based approach for data fusion and segmentation of multimodal images
G Iyer, J Chanussot, AL Bertozzi - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
In the past few years, graph-based methods have proven to be a useful tool in a wide variety
of energy minimization problems. In this article, we propose a graph-based algorithm for …
of energy minimization problems. In this article, we propose a graph-based algorithm for …
Low-rank decomposition and total variation regularization of hyperspectral video sequences
Hyperspectral video sequences (HVSs) are well suited for gas plume detection (GPD). The
high spectral resolution allows the detection of chemical clouds even when they are optically …
high spectral resolution allows the detection of chemical clouds even when they are optically …
Hyperspectral image classification using graph clustering methods
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 …
can range from hundreds to over a thousand frequencies depending on the sensor. Most …