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Superpixel construction for hyperspectral unmixing
2018 26th European Signal Processing Conference (EUSIPCO), 2018Spectral 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
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Error analysis in hyperspectral unmixing
SPIE Proceedings, 2004The 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
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Sparse filtering based hyperspectral unmixing
2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2016This 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
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Examining hyperspectral unmixing error reduction due to stepwise unmixing
SPIE Proceedings, 2003Unmixing 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 +2 more
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Hyperspectral Unmixing Using Deep Learning
2019 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR), 2019Due 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
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Cauchy NMF for Hyperspectral Unmixing
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 2020Non-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 +3 more
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Novel method for hyperspectral unmixing: fuzzy c-means unmixing
Sensor Review, 2016Purpose 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 +8 more
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Sparse Distributed Multitemporal Hyperspectral Unmixing
IEEE Transactions on Geoscience and Remote Sensing, 2017Blind 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 +3 more
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Band selection based hyperspectral unmixing
2009 IEEE International Workshop on Imaging Systems and Techniques, 2009Hyperspectral 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
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Variational methods for spectral unmixing of hyperspectral unmixing
2011International ...
Eches, Olivier +3 more
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