Results 101 to 110 of about 309,259 (214)

Stationary Subspace Analysis for Spatial Data

Environmetrics, Volume 37, Issue 6, September 2026.
ABSTRACT Stationary subspace analysis (SSA) is a blind source separation framework that decomposes linearly mixed multivariate data into stationary and nonstationary components. We extend SSA to spatially indexed data by introducing spatial stationary subspace analysis (spSSA), which explicitly accounts for spatial dependence.
Perttu Saarela, Klaus Nordhausen, Jaakko Pere, Anne M. Ruiz   +3 more
wiley   +1 more source

HYPERSPECTRAL CHANGE DETECTION BY SPARSE UNMIXING WITH DICTIONARY PRUNING

, 2015
Hyperspectral change detection is used in many applications ranging from environmental monitoring to city planning and military surveillance. Change detection by unmixing has the potential of not only providing the locations of the changes, but also the ...
Iordache, Marian-Daniel, Ertiirk, Alp, Plaza, Antonio   +2 more
core   +1 more source

Hyperspectral image unmixing using a multiresolution sticky HDP [PDF]

, 2012
This paper is concerned with joint Bayesian endmember extraction and linear unmixing of hyperspectral images using a spatial prior on the abundance vectors.We propose a generative model for hyperspectral images in which the abundances are sampled from a ...
Hero, Alfred O., Dobigeon, Nicolas, Hero, Alfred, Mittelman, Roni   +3 more
core   +1 more source

Urban Land Cover Mapping from Airborne Hyperspectral Imagery Using a Fast Jointly Sparse Spectral Mixture Analysis Method

Canadian Journal of Remote Sensing, 2020
Due to the fragmented compositional structure of urban scenes, many pixels are mixtures of multiple materials even in high spatial resolution airborne hyperspectral data.
Fen Chen, Sijia Lu, Peng Zhao, Tim Van de Voorde, Wenbo Xu   +4 more
doaj   +1 more source

Centralized Collaborative Sparse Unmixing for Hyperspectral Images

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017
Spectral unmixing is very important in hyperspectral image analysis and processing, which aims at identifying the constituent spectra (i.e., endmembers) and estimating their fractional abundances from the mixed pixels. In recent years, sparse unmixing has received considerable interest.
Rui Wang 0090, Heng-Chao Li 0001, Wenzhi Liao, Xin Huang 0002, Wilfried Philips   +4 more
openaire   +1 more source

Evolutionary Multiobjective Optimization with Endmember Priori Strategy for Large-Scale Hyperspectral Sparse Unmixing

, 2021
Mixed pixels inevitably appear in the hyperspectral image due to the low resolution of the sensor and the mixing of ground objects. Sparse unmixing, as an emerging method to solve the problem of mixed pixels, has received extensive attention in recent ...
Jianzhao Li, Peng Li, Fei Xie, Zhao Wang, Jinxin Wei   +4 more
core   +1 more source

NMF-based sparse unmixing of complex mixtures

, 2019
International audienceIn this work, we are interested in unmixing complex mixtures based on Nuclear Magnetic Resonance spectroscopy spectra. More precisely, we propose to solve a 2D blind source separation problem where signals (spectra) are highly ...
Chaux, Caroline, Piersanti, Elena, Cherni, Afef   +2 more
core   +2 more sources

Unsupervised Bayesian linear unmixing of gene expression microarrays [PDF]

, 2013
Background: This paper introduces a new constrained model and the corresponding algorithm, called unsupervised Bayesian linear unmixing (uBLU), to identify biological signatures from high dimensional assays like gene expression microarrays. The basis for
Ginsburg, Geoffrey S, Hero, Alfred O., Bazot, Cécile, Dobigeon, Nicolas, Tourneret, Jean-Yves, O Hero III, Alfred, Zaas, Aimee K., Zaas, Aimee K, Ginsburg, Geoffrey S.   +8 more
core   +1 more source

Classification of hyperspectral images with very small training size using sparse unmixing

, 2016
: Hyperspectral images are high dimensional while the available number of training samples can be very low. For very small training sizes, classical supervised classification strategies may fail.
Liao, WenzhiTW070000918519268020008033180000-0002-2183-03242434B67E-F0EE-11E1-A9DE-61C894A0A6B4, Heylen, Rob, Scheunders, Paul, Liao, Wenzhi, Rob Heylen, Wenzhi Liao, Philips, WilfriedTW078010007391760000-0003-4456-4353F4733938-F0ED-11E1-A9DE-61C894A0A6B4, Andrejchenko, Vera, Wilfried Philips, Philips, Wilfried, Vera Andrejchenko, Paul Scheunders   +11 more
core   +1 more source

Synthesizing hyperspectral Data using generative Models to train spectral Unmixing Methods for low-cost Crop Residue Cover Mapping [PDF]

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
The spectral range of the Field Imaging Nanosatellite for Crop residue Hyperspectral Mapping (FINCH), developed by the University of Toronto Aerospace Team’s Space System Division, poses significant challenges for hyperspectral unmixing to ...
E. Artan, E. Artan, S. Chen, S. Chen, A. Peng, A. Peng, S. A. Karya, S. A. Karya, K. Thiha, K. Thiha   +9 more
doaj   +1 more source