[PDF] Top 20 A Comparative Analysis of Hyperspectral Target Detection Algorithms in the Presence of Misregistered Data
Has 10000 "A Comparative Analysis of Hyperspectral Target Detection Algorithms in the Presence of Misregistered Data" found on our website. Below are the top 20 most common "A Comparative Analysis of Hyperspectral Target Detection Algorithms in the Presence of Misregistered Data".
A Comparative Analysis of Hyperspectral Target Detection Algorithms in the Presence of Misregistered Data
... scanning hyperspectral imaging systems are capable of capturing accurate spatial and spectral information about a ...These data can be useful for detecting sub-pixel ...collect data from multiple ... See full document
156
Comparative Assessment of Some Target Detection Algorithms for Hyperspectral Images
... IntroductIon Hyperspectral images portray a continuous spectrum of each pixel by capturing the data in several narrow and contiguous spectral ...a detection problem can be formulated either as ... See full document
10
A Quantitative and Comparative Analysis of Endmember Extraction Algorithms From Hyperspectral Data
... of algorithms have been developed over the past decade to accomplish the task of finding appropriate image end- members for spectral mixture ...the presence of pure class pixels in the image data ... See full document
14
Weighted Chebyshev Distance Algorithms for Hyperspectral Target Detection and Classification Applications
... For target detection applications, background classes consist of different types of ...So, target detection algorithms are different from classification algorithms by way of ... See full document
17
A Comparative Analysis of Kernel Subspace Target Detectors for Hyperspectral Imagery
... kernel-based detection algorithms the data is assumed to be implicitly mapped into a high-dimensional kernel feature space by a nonlinear mapping, which is associated with a kernel ...each ... See full document
13
Comparative Analysis of Covariance Matrix Estimation for Anomaly Detection in Hyperspectral Images
... the data when n does not greatly exceed d. Additionally, in the presence of multiple clusters, this estimation fails to characterize correctly the ...synthetic data from distribution with covariance ... See full document
17
Hyperspectral Image Processing for Automatic Target Detection Applications
... development, analysis, and application of detection algorithms to exploit hyperspectral imaging ...the hyperspectral data and explain how these data influence the signal ... See full document
38
Determination of target detection limits in hyperspectral data using band selection and dimensionality reduction
... sensing data can be used for civil and military applications to robustly detect and classify target ...of hyperspectral data can compensate for the comparatively low spatial resolution, which ... See full document
9
Matched filter stochastic background characterization for hyperspectral target detection
... a comparative analysis of stochastic background characterization techniques have led to many observations about the way each method attempts to match the source of detection interference, improve the ... See full document
202
Hyperspectral Imagery Target Detection Using Improved Anomaly Detection and Signature Matching Methods
... anomaly detection is viewed in this light, it would seem natural that the numerous outlier detection methods and principles proposed in the statistical literature are applicable to finding ... See full document
390
A manifold learning approach to target detection in high-resolution hyperspectral imagery
... of data, most nonlinear algorithms are local methods that only enforce local neighborhood ...the data, rather than in local ...real data on a nonlinear model, due to variations in real ... See full document
168
Clusters versus GPUs for Parallel Target and Anomaly Detection in Hyperspectral Images
... parallel algorithms for target and anomaly detection in hyperspectral image ...for target detection and classification (ATDCA), we have investigated the impact of including ... See full document
18
Assessment of target detection limits in hyperspectral data
... Abstract: Hyperspectral remote sensing data can be used for civil and military applications to detect and classify target objects that cannot be reliably separated using broadband ...the ... See full document
11
Essays on hyperspectral image analysis: classification and target detection
... In this context, we believe that, in order to develop a dictionary with high dis- criminative power for HSI classification but from only a limited number of labelled training samples, it is a promising direction to ... See full document
209
Hyperspectral sub-pixel target detection using hybrid algorithms and Physics Based Modeling
... This Dissertation is brought to you for free and open access by the Thesis/Dissertation Collections at RIT Scholar Works. It has been accepted for inclusion in Theses by an authorized administrator of RIT Scholar Works. ... See full document
231
Automatic Target Detection for Sparse Hyperspectral Images
... Component Analysis (RPCA), a given hyperspectral image (HSI) is regarded as being made up of the sum of low-rank background HSI and a sparse target HSI that contains the targets based on a ... See full document
29
COMPARATIVE STUDY AND ANALYSIS OF FACE DETECTION ALGORITHMS
... Face detection, Face Recognition, Image Processing, ROI region of interest, VIOLA JONES Face Detector I INTRODUCTION With the rapid increase of computational powers and availability of modern sensing, ... See full document
7
Comparison of Hyperspectral Imagery Target Detection Algorithm Chains
... AFAR) graphs for the VF1 target that employed the ACORN atmospheric compensation algorithm and the OSP matched filter... ROC Curves and AFAR Data[r] ... See full document
124
Matched shrunken subspace detectors for hyperspectral target detection
... to target detection from hyperspectral ...the target and background subspaces in the hypothesis models of the matched subspace detector (MSD), a popular subspace-based approach to ... See full document
32
Fusion Schemes for Ensembles of Hyperspectral Anomaly Detection Algorithms
... The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining ... See full document
75
Related subjects