Results 141 to 150 of about 1,407 (163)

Unmixing sparse hyperspectral mixtures

2009 IEEE International Geoscience and Remote Sensing Symposium, 2009
Finding an accurate sparse approximation of a spectral vector described by a linear model, when there is available a library of possible constituent signals (called endmembers or atoms), is a hard combinatorial problem which, as in other areas, has been increasingly addressed. This paper studies the efficiency of the sparse regression techniques in the
Marian-Daniel Iordache, Jose Bioucas-Dias, Antonio Plaza   +2 more
openaire   +1 more source

Spatially Adaptive Hyperspectral Unmixing

IEEE Transactions on Geoscience and Remote Sensing, 2011
Spectral unmixing is a common task in hyperspectral data analysis. In order to sufficiently spectrally unmix the data, three key steps must be accomplished: Estimate the number of endmembers (EMs), identify the EMs, and then unmix the data. Several different statistical and geometrical approaches have been developed for all steps of the unmixing ...
Kelly Canham, Ariel Schlamm, Amanda Ziemann, Bill Basener, David Messinger   +4 more
openaire   +1 more source

A Sturdy Nonlinear Hyperspectral Unmixing

IETE Journal of Research, 2020
Hyperspectral unmixing (HSU) is a way to process the prediction of the existing endmembers and the fractional abundances (FA) available in all pixels in the hyperspectral images.
M. Venkata Sireesha, P. V. Naganjaneyulu, K. Babulu   +2 more
openaire   +1 more source

Segmentation-based cNMF for hyperspectral unmixing

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017
This paper presents a modification to the cNMF for unmixing where the image is first segmented and the cNMF is applied to individual segments for endmember extraction. Extracted spectral endmembers from individual segments are clustered in endmember classes to describe the entire image. The approach is compared with the global cNMF.
Alkhatib, Mohammed Q., Velez-Reyes, Miguel   +1 more
openaire   +1 more source

Superpixel construction for hyperspectral unmixing

2018 26th European Signal Processing Conference (EUSIPCO), 2018
Spectral unmixing aims to determine the component materials and their associated abundances from mixed pixels in a hyperspectral image. Instead of performing unmixing independently on each pixel, investigating spatial and spectral correlations among pixels can be beneficial to enhance the unmixing performance.
Zeng Li, Jie Chen, Susanto Rahardja
openaire   +1 more source

Error analysis in hyperspectral unmixing

SPIE Proceedings, 2004
The estimation of abundance coefficients, or unmixing, of hyperspectral data is important in a wide variety of applications. Assuming the major constituents, or endmembers, of a scene are known, the unmixing problem is relatively straightforward and easily solved using least-squares techniques. What is less well understood, however, is how error in the
David Gillis, Jeffrey Bowles
openaire   +1 more source

Sparse filtering based hyperspectral unmixing

2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2016
This work proposes a hyperspectral unmixing technique based on sparse filtering approach. The proposed method exploits the sparsity of feature distribution rather than modeling the data distribution. The proposed sparse filtering based unmixing procedure is essentially parameter-free, and the only parameter is to find the number of endmembers to be ...
Hemant Kumar Aggarwal, Angshul Majumdar
openaire   +1 more source

Examining hyperspectral unmixing error reduction due to stepwise unmixing

SPIE Proceedings, 2003
Unmixing hyperspectral images inherently transfers error from the original hyperspectral image to the unmixed fraction plane image. In essence by reducing the entire information content of an image down to a handful of representative spectra a significant amount of information is lost.
Michael E. Winter, Paul G. Lucey, Donovan Steutel   +2 more
openaire   +1 more source

Hyperspectral Unmixing Using Deep Learning

2019 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR), 2019
Due to factors such as low spatial resolution, microscopic material mixing, and multiple scattering, hyperspectral images generally have problems with mixed pixels. This paper proposes two network structures under the framework of deep learning, which can be well applied to hyperspectral unmixing: 1) network architecture based on spectral information ...
Chen-Jian Wang, Hong Li, Yuan-Yan Tang
openaire   +1 more source

Cauchy NMF for Hyperspectral Unmixing

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 2020
Non-negative matrix factorization (NMF) is a classical hyperspectral unmixing model which minimizes the Euclidean distance between the hyperspectral data matrix and its low rank approximation (i.e., the product of endmember matrix and abundance matrix), and it fails when applied to noisy data because the loss function is sensitive to outliers.
Jiangtao Peng, Fan Jiang, Weiwei Sun, Yicong Zhou   +3 more
openaire   +1 more source