منابع مشابه
Effective Spectral Unmixing via Robust Representation and Learning-based Sparsity
Hyperspectral unmixing (HU) plays a fundamental role in a wide range of hyperspectral applications. It is still very challenging due to the common presence of outlier channels and the large solution space. To address the above two issues, we propose a novel model by emphasizing both robust representation and learning-based sparsity. Specifically, we apply the `2,1-norm to measure the representa...
متن کاملSparsity Constrained Graph Regularized NMF for Spectral Unmixing of Hyperspectral Data
Hyperspectral images contain mixed pixels due to low spatial resolution of hyperspectral sensors. Mixed pixels are pixels containing more than one distinct material called endmembers. The presence percentages of endmembers in mixed pixels are called abundance fractions. Spectral unmixing problem refers to decomposing these pixels into a set of endmembers and abundance fractions. Due to nonnegat...
متن کاملEstimating the Number of Endmembers to Use in Spectral Unmixing of Hyperspectral Data with Collaborative Sparsity
Spectral Umixing (SU) in hyperspectral remote sensing aims at recovering the signatures of the pure materials in the scene (endmembers) and their abundances in each pixel of the image. The usual SU chain does not take spectral variability (SV) into account, and relies on the estimation of the Intrinsic Dimensionality (ID) of the data, related to the number of endmembers (NOE) to use. However, t...
متن کاملDistributed Unmixing of Hyperspectral Data With Sparsity Constraint
Spectral unmixing (SU) is a data processing problem in hyperspectral remote sensing. The significant challenge in the SU problem is how to identify endmembers and their weights, accurately. For estimation of signature and fractional abundance matrices in a blind problem, nonnegative matrix factorization (NMF) and its developments are used widely in the SU problem. One of the constraints which w...
متن کاملAn Overview of Nonlinear Spectral Unmixing Methods in the Processing of Hyperspectral Data
The hyperspectral imagery provides images in hundreds of spectral bands within different wavelength regions. This technology has increasingly applied in different fields of earth sciences, such as minerals exploration, environmental monitoring, agriculture, urban science, and planetary remote sensing. However, despite the ability of these data to detect surface features, the measured spectrum i...
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ژورنال
عنوان ژورنال: IEEE Transactions on Image Processing
سال: 2014
ISSN: 1057-7149,1941-0042
DOI: 10.1109/tip.2014.2363423