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[PDF] Top 20 Characterization of the spectral distribution of hyperspectral imagery for improved exploitation

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Characterization of the spectral distribution of hyperspectral imagery for improved exploitation

Characterization of the spectral distribution of hyperspectral imagery for improved exploitation

... 1.1 Spectral Image Analysis The collection and use of spectral imagery has grown over the years as the modality inherently measures spatial and spectral information over the full ...Existing ... See full document

220

Exploitation of Intra-Spectral Band Correlation for Rapid Feacture Selection and Target Identification in Hyperspectral Imagery

Exploitation of Intra-Spectral Band Correlation for Rapid Feacture Selection and Target Identification in Hyperspectral Imagery

... within hyperspectral imagery ...associate spectral rather than spatial dimensions. By seeking similar spectral dimensions, the assumption of no a priori knowledge of the relationship between ... See full document

191

A Review of Unsupervised Spectral Target Analysis for Hyperspectral Imagery

A Review of Unsupervised Spectral Target Analysis for Hyperspectral Imagery

... a spectral target defined as above what we are particularly interested in this paper from an aspect of sta- tistical signal processing are two types of spectral targets, one characterized by 2nd order ... See full document

26

Urban Vegetation Mapping using Hyperspectral Imagery and Spectral Library

Urban Vegetation Mapping using Hyperspectral Imagery and Spectral Library

... better spectral characterization of the pixels when increasing the bands number, indeed, the intraclass variability was less impacted when increasing the training samples, and the differentiation between ... See full document

5

Hidden Markov model approach to spectral analysis for hyperspectral imagery

Hidden Markov model approach to spectral analysis for hyperspectral imagery

... nificantly improved capability of ground-based data collec- tion in many fields such as agriculture, geology, geography, law enforcement, defense, ...hyper- spectral sensors, 224-band AVIRIS 共airborne ... See full document

8

Characterization and Reduction of Noise in Manifold Representations of Hyperspectral Imagery

Characterization and Reduction of Noise in Manifold Representations of Hyperspectral Imagery

... local spectral subsets have spectral ...local spectral subsets contain one continuous man- ...in spectral space, guarantees spectral ...yet. Spectral subsetting will be shown to ... See full document

131

Clustering Hyperspectral Imagery for Improved Adaptive Matched Filter Performance

Clustering Hyperspectral Imagery for Improved Adaptive Matched Filter Performance

... a spectral library consisting of ground-measured reflectance data from objects of ...with spectral signature matching is that hyperspectral images are collected using a sensor that collects ... See full document

16

Spectral Detection of Acute Mental Stress with VIS-SWIR Hyperspectral Imagery

Spectral Detection of Acute Mental Stress with VIS-SWIR Hyperspectral Imagery

... T his chapter covers the results and analysis of the experimental procedures conducted in this thesis. The experiment is executed according to Section 3.1.4 with results from training and testing outlined in Section 3.5. ... See full document

150

Novel Pattern Recognition Techniques for Improved Target Detection in Hyperspectral Imagery

Novel Pattern Recognition Techniques for Improved Target Detection in Hyperspectral Imagery

... of hyperspectral imaging (HSI) ...as imagery taken over many spectrally contiguous and spatially co-registered ...of spectral information to uniquely identify materials by their reflectance ...and ... See full document

213

Calibration and Characterization of Hyperspectral Imaging Systems Used for Natural Scene Imagery

Calibration and Characterization of Hyperspectral Imaging Systems Used for Natural Scene Imagery

... and characterization of focal plane hyperspectral imaging systems play an important role in natural scene ...the spectral characteristics of the imaging system as well as its geometric properties be ... See full document

18

Semi-supervised linear spectral unmixing using a hierarchical Bayesian model for hyperspectral imagery

Semi-supervised linear spectral unmixing using a hierarchical Bayesian model for hyperspectral imagery

... The complexity of the posterior distributions for the unknown parameters requires to use appropriate simulation methods such as Markov chain Monte Carlo (MCMC) methods [8]. The prior distributions used in the present ... See full document

12

Supervised nonlinear spectral unmixing using a post-nonlinear mixing model for hyperspectral imagery

Supervised nonlinear spectral unmixing using a post-nonlinear mixing model for hyperspectral imagery

... the spectral libraries provided with the ENVI software [28] ...uniform distribution over the admissible set defined by the positivity and sum-to-one ... See full document

10

Multiscale spatial-spectral convolutional network with image-based framework for hyperspectral imagery classification

Multiscale spatial-spectral convolutional network with image-based framework for hyperspectral imagery classification

... In our second test using the Indian Pines dataset, we validated the performances of our proposed method using a different number of training samples. The sensitivity of the model to the number of samples was evaluated by ... See full document

21

Spectral Detection of Human Skin in VIS-SWIR Hyperspectral Imagery without Radiometric Calibration

Spectral Detection of Human Skin in VIS-SWIR Hyperspectral Imagery without Radiometric Calibration

... irradiance distribution, atmo- spheric transmission, scene geometry, object spectral reflectance, and scene-to-sensor path ...jacent spectral bands to provide high-dimensional HSI data, which often ... See full document

114

Spectral-spatial feature learning for hyperspectral imagery classification using deep stacked sparse autoencoder

Spectral-spatial feature learning for hyperspectral imagery classification using deep stacked sparse autoencoder

... of hyperspectral imagery contains data input, hierarchical deep learning model training, and classification ...of spectral, spatial, or joint utilization of spectral – spatial ...probability ... See full document

16

Multi-task sparse nonnegative matrix factorization for joint spectral-spatial hyperspectral imagery denoising

Multi-task sparse nonnegative matrix factorization for joint spectral-spatial hyperspectral imagery denoising

... based model generating the atoms in a dictionary that contains various parts of the training samples. Such solution naturally favors sparse and parts-based representations [30], [31]. Matrix decomposition without ... See full document

35

Group sparse representation based on nonlocal spatial and local spectral similarity for hyperspectral imagery classification

Group sparse representation based on nonlocal spatial and local spectral similarity for hyperspectral imagery classification

... some spectral abnormal pixels (caused by the presence of same material with different spectra), GSRC probably misclassifies the testing ...local spectral similar area in a nonlocal spatial similar patch can ... See full document

19

Hyperspectral Imagery Target Detection Using Improved Anomaly Detection and Signature Matching Methods

Hyperspectral Imagery Target Detection Using Improved Anomaly Detection and Signature Matching Methods

... Grossman, Bowles, Haas, Antoniades, Grunes, Palmadesso, Gillis, Tsang, Baumback, Daniel, Fisher, and Triandaf (1998) present a mixture detector system called the Optical Real-time Adaptive Spectral Identification ... See full document

390

PET and PVC separation with hyperspectral Imagery

PET and PVC separation with hyperspectral Imagery

... The characterization of materials via the hyperspectral platform requires the following ...steps. Imagery acquisition and ...high-resolution spectral image contains then line pixels in a ... See full document

23

Improved sparse representation using adaptive spatial support for effective target detection in hyperspectral imagery

Improved sparse representation using adaptive spatial support for effective target detection in hyperspectral imagery

... representation, hyperspectral imagery, target detection, adaptive spatial support, remote sensing ...Introduction Hyperspectral imagery, through combining imaging and spectrum techniques, ... See full document

16

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