Results 151 to 160 of about 1,407 (163)

Novel method for hyperspectral unmixing: fuzzy c-means unmixing

Sensor Review, 2016
Purpose This paper aims to effectively achieve endmembers and relative abundances simultaneously in hyperspectral image unmixing yield. Hyperspectral unmixing, which is an important step before image classification and recognition, is a challenging issue because of the limited resolution of image sensors and the complex diversity of nature.
Mingyu Nie, Zhi Liu, Xiaomei Li, Qiang Wu, Bo Tang, Xiaoyan Xiao, Yulin Sun, Jun Chang, Chengyun Zheng   +8 more
openaire   +1 more source

Sparse Distributed Multitemporal Hyperspectral Unmixing

IEEE Transactions on Geoscience and Remote Sensing, 2017
Blind hyperspectral unmixing jointly estimates spectral signatures and abundances in hyperspectral ima-ges (HSIs). Hyperspectral unmixing is a powerful tool for analyzing hyperspectral data. However, the usual huge size of HSIs may raise difficulties for classical unmixing algorithms, namely, due to limitations of the hardware used.
Jakob Sigurdsson, Magnus O. Ulfarsson, Johannes R. Sveinsson, Jose M. Bioucas-Dias   +3 more
openaire   +1 more source

Band selection based hyperspectral unmixing

2009 IEEE International Workshop on Imaging Systems and Techniques, 2009
Hyperspectral unmixing is the procedure by which the measured spectrum of a mixed pixel is decomposed into a collection of constituent spectra, or endmembers, and their mixing proportions. However, due to the hundreds of spectral bands contained in the hyperspectral imagery, the large amount of data not only increase the computational loads, but also ...
Sen Jia, Zhen Ji, Yuntao Qian
openaire   +1 more source

Variational methods for spectral unmixing of hyperspectral unmixing

2011
International ...
Eches, Olivier, Dobigeon, Nicolas, Tourneret, Jean-Yves, Snoussi, Hichem   +3 more
openaire   +2 more sources

Algorithm taxonomy for hyperspectral unmixing

SPIE Proceedings, 2000
In this paper, we introduce a set of taxonomies that hierarchically organize and specify algorithms associated with hyperspectral unmixing. Our motivation is to collectively organize and relate algorithms in order to assess the current state-of-the-art in the field and to facilitate objective comparisons between methods.
Nirmal Keshava, John P. Kerekes, Dimitris G. Manolakis, Gary A. Shaw   +3 more
openaire   +1 more source

Feature Extraction-Based Hyperspectral Unmixing

2019
Purpose Hyperspectral imaging belongs to a class of techniques called spectral imaging or spectral analysis. Due to the high dimensionality of hyperspectral cubes, it is a very difficult task to select few informative bands from original hyperspectral remote sensing images.
M. R. Vimala Devi, S. Kalaivani
openaire   +1 more source

Unsupervised Unmixing of Hyperspectral Imagery

2006 49th IEEE International Midwest Symposium on Circuits and Systems, 2006
This paper presents an approach for simultaneous determination of end members and their abundances in hyperspectral imagery using a constrained positive matrix factorization. The algorithm presented here solves the constrained PMF using Gauss-Seidel method.
Yahya M. Masalmah, Miguel Velez-Reyes
openaire   +1 more source

KMNET for Hyperspectral Unmixing

2023 IEEE India Geoscience and Remote Sensing Symposium (InGARSS), 2023
Sankalp Dhondi, T. Hitendra Sarma, Abignya Reddy, S. Sree Lakshmi, K. Ram Mohan Rao   +4 more
openaire   +1 more source

Dependent Component Analysis: A Hyperspectral Unmixing Algorithm

2007
Linear unmixing decomposes a hyperspectral image into a collection of reflectance spectra of the materials present in the scene, called endmember signatures, and the corresponding abundance fractions at each pixel in a spatial area of interest. This paper introduces a new unmixing method, called Dependent Component Analysis (DECA), which overcomes ...
Nascimento, Jose, Bioucas-Dias, José M.
openaire   +2 more sources

Superpixel based unmixing for enhanced hyperspectral denoising

2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2016
Unmixing based denoising for hyperspectral images is a recent addition to the literature, and aims to reconstruct the data using noise-free and pure spectral signatures and their abundances. Unmixing based denoising has the potential of providing enhanced denoising performance by excluding the noise effects in the endmember and abundance matrices, and ...
openaire   +2 more sources