Journal ArticleDOI
Hybrid Detectors for Subpixel Targets
Joshua Broadwater,Rama Chellappa +1 more
TLDR
Results demonstrate improved performance over the well-known AMSD and ACE subpixel algorithms in experiments that include multiple targets, images, and area types - especially when dealing with weak targets in complex backgrounds.Abstract:
Subpixel detection is a challenging problem in hyperspectral imagery analysis. Since the target size is smaller than the size of a pixel, detection algorithms must rely solely on spectral information. A number of different algorithms have been developed over the years to accomplish this task, but most detectors have taken either a purely statistical or a physics-based approach to the problem. We present two new hybrid detectors that take advantage of these approaches by modeling the background using both physics and statistics. Results demonstrate improved performance over the well-known AMSD and ACE subpixel algorithms in experiments that include multiple targets, images, and area types - especially when dealing with weak targets in complex backgrounds.read more
Citations
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Journal ArticleDOI
Hyperspectral Remote Sensing Image Subpixel Target Detection Based on Supervised Metric Learning
TL;DR: A novel supervised metric learning (SML) algorithm is proposed, which can effectively learn a distance metric for hyperspectral target detection, by which target pixels are easily detected in positive space while the background pixels are pushed into negative space as far as possible.
Journal ArticleDOI
Blind Spectral Unmixing Based on Sparse Nonnegative Matrix Factorization
TL;DR: A novel measure (termed as S-measure) of sparseness using higher order norms of the signal vector is proposed in this paper, and features the physical significance.
Journal ArticleDOI
Hierarchical Suppression Method for Hyperspectral Target Detection
Zhengxia Zou,Zhenwei Shi +1 more
TL;DR: Experimental results suggest that the proposed hierarchical method to suppress the backgrounds while preserving the target spectra significantly improves the performance of the original CEM detection algorithm and also outperforms other classical and recently proposed hyperspectral target detection algorithms.
Journal ArticleDOI
Integration of Spatial–Spectral Information for Resolution Enhancement in Hyperspectral Images
TL;DR: The experimental results prove that the proposed algorithm effectively enhances the resolution of HSIs and indicate its applicability.
Journal ArticleDOI
Fuzzy Spectral and Spatial Feature Integration for Classification of Nonferrous Materials in Hyperspectral Data
TL;DR: The proposed FUSSER (fuzzy spectral and spatial classifier) algorithm merges the spectral andatial features to obtain a combined feature vector that is able to better sample the properties of the nonferrous materials than the single pixel spectral features when applied to the construction of multivariate Gaussian distributions.
References
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Book
Theory of Reflectance and Emittance Spectroscopy
TL;DR: In this article, the authors present a review of vector calculus and functions of a complex variable and Fraunhoffer diffraction by a circular hole, and a miscellany of bidirectional reflectances and related quantities.
Journal ArticleDOI
Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery
D.C. Heinz,Chein-I-Chang +1 more
TL;DR: The authors present a fully constrained least squares (FCLS) linear spectral mixture analysis method for material quantification, where no closed form can be derived for this method and an efficient algorithm is developed to yield optimal solutions.
Journal ArticleDOI
Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach
J.C. Harsanyi,Chein-I Chang +1 more
TL;DR: A technique which simultaneously reduces the data dimensionality, suppresses undesired or interfering spectral signatures, and detects the presence of a spectral signature of interest is described.
Journal ArticleDOI
An Adaptive Detection Algorithm
TL;DR: A likelihood ratio decision rule is derived and its performance evaluated in both the noise-only and signal-plus-noise cases.
Journal ArticleDOI
Detection algorithms for hyperspectral imaging applications
Dimitris G. Manolakis,G. Shaw +1 more
TL;DR: This work focuses on detection algorithms that assume multivariate normal distribution models for HSI data and presents some results which illustrate the performance of some detection algorithms using real hyperspectral imaging (HSI) data.
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Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach
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