Spectral Imaging for Remote Sensing

By Gary A. Shaw, Hsiao-hua K. Burke. 2003

608 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/26ffa240d824f1503f3afafdc14e4a1711449138

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Citations in the corpus

• Hyperspectral Unmixing via $L_{1/2}$ Sparsity-Constrained Nonnegative Matrix Factorization (2011, methodology): “Linear spectral mixture model The classical linear mixing model, which is used to represent the spectrum of a pixel of L wavelength-indexed bands in the observed scene [29] based upon K end-member abundances is given by”
• Modern Trends in Hyperspectral Image Analysis: A Review (2018, background): “In each case spectral response will be shifted accordingly showing the presence of suspended sediments or algae in water [22].”
• Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches (2012, unknown): “Some of this variability is amplitude-based and therefore primarily characterized by spectral shape invariance [38]; i.”
• An Efficient Pan-Sharpening Method via a Combined Adaptive PCA Approach and Contourlets (2008, background): “The spectral and spatial resolutions have an inverse relationship [1].”
• Constrained Nonnegative Matrix Factorization for Hyperspectral Unmixing (2009, background): “is a necessary preprocessing step for hyperspectral applications [1]–[4].”
• Does independent component analysis play a role in unmixing hyperspectral data? (2003, methodology): “This model takes into account the degradation mechanisms normally found in hyperspectral applications, namely signature variability [38]–[40], abundance constraints, topography modulation, and system noise.”
• Fusing Hyperspectral and Multispectral Images via Coupled Sparse Tensor Factorization (2018, background): “In this way, the signal-to-noise-ratio (SNR) due to the Poisson noise is kept above a mini-Manuscript mum acceptable value [7].”
• Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress (2017, background): “For aerial remote sensing data, the spatial resolution is typically in the range of meters per pixel, which means the pixels will usually contain signatures from more than one material [14, 15].”