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Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
TLDR
This paper presents an overview of un Mixing methods from the time of Keshava and Mustard's unmixing tutorial to the present, including Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixed algorithms. Expand
Sparse Unmixing of Hyperspectral Data
TLDR
The experimental results, conducted using both simulated and real hyperspectral data sets collected by the NASA Jet Propulsion Laboratory's Airborne Visible Infrared Imaging Spectrometer and spectral libraries publicly available from the U.S. Geological Survey, indicate the potential of SR techniques in the task of accurately characterizing the mixed pixels using the library spectra. Expand
Total Variation Spatial Regularization for Sparse Hyperspectral Unmixing
TLDR
The total variation (TV) regularization to the classical sparse regression formulation is included, thus exploiting the spatial-contextual information present in the hyperspectral images and developing a new algorithm called sparse unmixing via variable splitting augmented Lagrangian and TV. Expand
Land Surface Emissivity Retrieval From Different VNIR and TIR Sensors
TLDR
The application and adaptation of two existing operational algorithms for land surface emissivity retrieval from different operational satellite/airborne sensors with bands in the visible and near-infrared (VNIR) and thermal IR (TIR) regions are discussed. Expand
Hyperspectral Image Segmentation Using a New Bayesian Approach With Active Learning
TLDR
A new supervised Bayesian approach to hyperspectral image segmentation with active learning, which consists of a multinomial logistic regression model to learn the class posterior probability distributions and a new active sampling approach, called modified breaking ties, which is able to provide an unbiased sampling. Expand
Collaborative Sparse Regression for Hyperspectral Unmixing
TLDR
This paper adopts the collaborative (also called “multitask” or “simultaneous”) sparse regression framework that improves the unmixing results by solving a joint sparse regression problem, where the sparsity is simultaneously imposed to all pixels in the data set. Expand
Spectral–Spatial Hyperspectral Image Segmentation Using Subspace Multinomial Logistic Regression and Markov Random Fields
TLDR
This paper introduces a new supervised segmentation algorithm for remotely sensed hyperspectral image data which integrates the spectral and spatial information in a Bayesian framework and represents an innovative contribution in the literature. Expand
Recent Advances in Techniques for Hyperspectral Image Processing
TLDR
A seminal view on recent advances in techniques for hyperspectral image processing, focusing on the design of techniques able to deal with the high-dimensional nature of the data, and to integrate the spa- tial and spectral information. Expand
Generalized Composite Kernel Framework for Hyperspectral Image Classification
TLDR
A new family of generalized composite kernels which exhibit great flexibility when combining the spectral and the spatial information contained in the hyperspectral data, without any weight parameters are constructed. Expand
Hyperspectral Remote Sensing Data Analysis and Future Challenges
TLDR
A tutorial/overview cross section of some relevant hyperspectral data analysis methods and algorithms, organized in six main topics: data fusion, unmixing, classification, target detection, physical parameter retrieval, and fast computing. Expand
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