Results 61 to 70 of about 345,137 (201)

Anomaly detection from hyperspectral imagery [PDF]

IEEE Signal Processing Magazine, 2002
We develop anomaly detectors, i.e., detectors that do not presuppose a signature model of one or more dimensions, for three clutter models: the local normal model, the global normal mixture model, and the global linear mixture model. The local normal model treats the neighborhood of a pixel as having a normal probability distribution.
Stein, D. W. J., Beaven, S. G., Hoff, L. E., Winter, E. M., Schaum, A. P., Stocker, A. D.   +5 more
openaire   +2 more sources

Residual component analysis of hyperspectral images - Application to joint nonlinear unmixing and nonlinearity detection [PDF]

, 2014
This paper presents a nonlinear mixing model for joint hyperspectral image unmixing and nonlinearity detection. The proposed model assumes that the pixel reflectances are linear combinations of known pure spectral components corrupted by an additional ...
Altmann, Yoann, Altmann, Yoann; id_orcid, Tourneret, Jean-yves, Tourneret, Jean Yves, Yoann Altmann, Steve Mclaughlin, McLaughlin, Steve; id_orcid, Nicolas Dobigeon, Jean-yves Tourneret, Dobigeon, Nicolas, McLaughlin, Steve   +10 more
core   +1 more source

Gudalur Spectral Target Detection (GST-D): A New Benchmark Dataset and Engineered Material Target Detection in Multi-Platform Remote Sensing Data

Remote Sensing, 2020
Target detection in remote sensing imagery, mapping of sparsely distributed materials, has vital applications in defense security and surveillance, mineral exploration, agriculture, environmental monitoring, etc. The detection probability and the quality
Sudhanshu Shekhar Jha, Rama Rao Nidamanuri   +1 more
doaj   +1 more source

Intelligent hyperspectral target detection for reliable IoV applications

EURASIP Journal on Wireless Communications and Networking, 2022
In recent years, hyperspectral imagery has played a significant role in IoV (Internet of Vehicles) vision areas such as target acquisition. Researchers are focusing on integrating detection sensors, detection computing units, and communication units into
Zixu Wang, Lizuo Jin, Kaixiang Yi
doaj   +1 more source

i.hyper: processing hyperspectral imagery in GRASS

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Abstract. Hyperspectral satellite missions such as EnMAP, PRISMA and Tanager have made imaging spectroscopy widely accessible, yet their heterogeneous formats, high dimensionality and demanding preprocessing requirements still hinder efficient scientific use.
Alen Mangafić, Tomaž Žagar
openaire   +2 more sources

Nonlinear unmixing of hyperspectral images using a generalized bilinear model [PDF]

, 2011
Nonlinear models have recently shown interesting properties for spectral unmixing. This paper studies a generalized bilinear model and a hierarchical Bayesian algorithm for unmixing hyperspectral images. The proposed model is a generalization not only of
Altmann, Yoann, Jean-Yves Tourneret, Halimi, Abderrahim; id_orcid, Altmann, Yoann; id_orcid, Halimi, Abderrahim, Abderrahim Halimi, Yoann Altmann, Nicolas Dobigeon, Dobigeon, Nicolas, Tourneret, Jean-Yves   +9 more
core   +1 more source

Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches [PDF]

, 2012
Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras.
Paul Gader, Jocelyn Chanussot, Qian Du, Gader, Paul, Antonio Plaza, José M. Bioucas-dias, Chanussot, Jocelyn, Dobigeon, Nicolas, Plaza, Antonio, Du, Qian, Mario Parente, Bioucas-Dias, José M., Nicolas Dobigeon, Parente, Mario   +13 more
core   +1 more source

Attention Residual Hybrid Network for Unmanned Aerial Vehicles Hyperspectral Image Classification

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Unmanned aerial vehicle (UAV) hyperspectral images are endowed with abundant spectral information and spatial texture details, which are crucial for the precise classification and monitoring of terrestrial features.
Zhen Zhang, Linhuan Jiang, Bo-Hui Tang, Jianchen Liu, Qingwang Wang, Yabin Hu, Liang Huang, Zhitao Fu   +7 more
doaj   +1 more source

Enhancing hyperspectral image unmixing with spatial correlations [PDF]

, 2010
This paper describes a new algorithm for hyperspectral image unmixing. Most unmixing algorithms proposed in the literature do not take into account the possible spatial correlations between the pixels.
Jean-Yves Tourneret, Eches, Olivier, Olivier Eches, Nicolas Dobigeon, Dobigeon, Nicolas, Tourneret, Jean-Yves   +5 more
core   +1 more source

Automated Labeling of Materials in Hyperspectral Imagery [PDF]

IEEE Transactions on Geoscience and Remote Sensing, 2010
We present a technique for automatically labeling segmented hyperspectral imagery with semantically meaningful material labels. The technique compares the mean signatures of each image segment to a spectral library of known materials, and material labels are assigned to image segments according to the most similar library entry.
Brian D. Bue, Erzsébet Merényi, Beáta Csathó   +2 more
openaire   +1 more source