Target Detection and Classification at Kernel Blitz 1997 Using Spectral Imagery PDF Download
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Author: Jeffrey D. Sanders
Publisher:
ISBN: 9781423553342
Category :
Languages : en
Pages : 115
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Book Description
Data collected from the Hyperspectral Digital Imagery Collection Experiment (HYDICE) were analyzed in this thesis to determine the feasibility of wide area detection and classification of target materials in the visible to short wave infrared region. This study used detection algorithms such as spectral angle mapper and matched filter for target detection. Parallelepiped and Maximum Likelihood routines were used to classify the image data for subsequent analyses and comparisons. Effects on data due to altitude variation of the sensor were analyzed using histograms, differencing, and principal component transforms. Data images of the Camp Pendleton airfield used for comparison analyses were obtained from two different altitudes, 5,000 feet and 10,000 feet. Results showed target detection and classification had no strong dependence on altitude.
Author: Jeffrey D. Sanders
Publisher:
ISBN: 9781423553342
Category :
Languages : en
Pages : 115
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Book Description
Data collected from the Hyperspectral Digital Imagery Collection Experiment (HYDICE) were analyzed in this thesis to determine the feasibility of wide area detection and classification of target materials in the visible to short wave infrared region. This study used detection algorithms such as spectral angle mapper and matched filter for target detection. Parallelepiped and Maximum Likelihood routines were used to classify the image data for subsequent analyses and comparisons. Effects on data due to altitude variation of the sensor were analyzed using histograms, differencing, and principal component transforms. Data images of the Camp Pendleton airfield used for comparison analyses were obtained from two different altitudes, 5,000 feet and 10,000 feet. Results showed target detection and classification had no strong dependence on altitude.
Author: William M. Arkin
Publisher: Little, Brown
ISBN: 0316323365
Category : Political Science
Languages : en
Pages : 381
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Book Description
Unmanned is an in-depth examination of why seemingly successful wars never seem to end. The problem centers on drones, now accumulated in the thousands, the front end of a spying and killing machine that is disconnected from either security or safety. Drones, however, are only part of the problem. William Arkin shows that security is actually undermined by an impulse to gather as much data as possible, the appetite and the theory both skewed towards the notion that no amount is too much. And yet the very endeavor of putting fewer human in potential danger places everyone in greater danger. Wars officially end, but the Data Machine lives on forever. Throughout his career, Arkin has exposed powerful secrets of so-called national security and intelligence. Now he continues that tradition. The most alarming book about warfare in years, Unmanned is essential reading for anyone who cares about the future of mankind.
Author: Miao, Xi
Publisher:
ISBN:
Category : Imaging systems
Languages : en
Pages : 120
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Book Description
Author: David Robert Perry
Publisher:
ISBN: 9781423547457
Category : Camouflage (Military science)
Languages : en
Pages : 179
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Book Description
Spectral imagery provides a new resource in remote sensing, which can be used for defeating camouflage, concealment and detection, as well as terrain categorization. A new sensor, the Night Vision Imaging Spectrometer (NVIS), provides VNIR/SWIR (0.4-2.5 m) spectra, which are used to here to study such applications. NVIS has a nominal GSD of 0.5- 1.5 meters in operational modes utilized for this work, which make the data well suited for studying mapping and classification algorithms. Data taken at Pt. A.P. Hill on April 29, 2000 are studied here. A Principal Components Transformation was performed on the NVIS data. From this new data set, target spectra were collected for use in classification algorithms. The NVIS data was converted from radiance to reflectance in two different ways: Empirical Line Method and Internal Average Relative Reflectance. Using this data, various standard algorithms were performed. It was found that while none of the algorithms correctly classified all of the selected targets, the Mahalanobis Distance and Mixture Tuned Matched Filter(TM) algorithms were the most successful.
Author: Shao-Shan Chiang
Publisher:
ISBN:
Category : Remote-sensing images
Languages : en
Pages : 258
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Author: Xiaoli Jiao
Publisher:
ISBN: 9781243759825
Category :
Languages : en
Pages : 140
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Book Description
Hyperspectral imaging has become an emerging technique in remote sensing analysis. With high spectral/spatial resolution many unknown material substances can be revealed by hyperspectral imaging sensors for data exploitation. While it significantly improved the capability for target detection it also threw great challenges in designing and developing effective methods to process data and to get the needed information for image analysis. The primary focus of this dissertation is the development of unsupervised approaches to target detection and classification for hyperspectral imagery. The research represents a significant departure from supervised approaches where the target information is assumed to be given or can be obtained a priori. Three major types of spectral targets are of particular interest in this dissertation. One is endmembers whose spectral signatures are idealistically pure. Another is anomaly which usually occurs in small size with signatures significantly different from image background. The third is human-made objects. All these types of spectral targets are usually appear in small population and occur with low probabilities, e.g., special spices in agriculture and ecology, toxic wastes in environmental monitoring, rare minerals in geology, drug/smuggler trafficking in law enforcement, combat vehicles in the battlefield, landmines in war zones, chemical/biological agents in bioterrorism, weapon concealment and mass graves. These spectral targets are generally considered as insignificant objects because of their very limited spatial information but they are actually critical and crucial for defense and intelligence analysis since they are insignificant compared to targets with large sample pools and generally hard to be identified by visual inspection. From a statistical point of view, the spectral information statistics of such special targets cannot be captured by 2nd order statistics as variances but rather by high-order statistics (HOS) as skewness, kurtosis and etc. In light of this interpretation we categorize the image pixels into two classes. One is background (BKG) with pixel spectral signature characterized by 2nd statistics. The other is target with pixel signature characterized by high-order statistics. Once image pixel vectors are categorized into BKG and target classes according to spectral properties, the follow-up task is how to find them. One is how many of them. The other is how to extract them. The key discovery that led to the techniques developed in the remaining chapters of this dissertation is the recognition that a data normalization technique called sphering can retain high order statistics but remove the 1st and 2nd order statistics of a data set. Three least-squares based unsupervised virtual endmember finding algorithms (LS-VEFA) and a component-analysis based unsupervised virtual endmember finding algorithms (CA-VEFA) are developed in this dissertation to extract target and background signatures for image analysis based on linear spectral mixture analysis (LSMA) model. In addition, to address the drawbacks of the current virtual dimensionality (VD) techniques which is originally designed to estimate the number of distinct signatures in a hyperspectral image, an orthogonal subspace projection approach has developed to find the number of signal resources in hyperspectral imagery with the implementation of two separately developed algorithms, unsupervised target sample generation (UTSG) algorithms and unsupervised background sample generation (UBSG) algorithms. The mathematical basis of these new processing approaches is the concept of subspace projection. Projection of each pixel in a...
Author: Wesam Adel Sakla
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
A fundamental challenge in target detection in hyperspectral imagery is spectral variability. In target detection applications, we are provided with a pure target signature; we do not have a collection of samples that characterize the spectral variability of the target. Another problem is that the performance of stochastic detection algorithms such as the spectral matched filter can be detrimentally affected by the assumptions of multivariate normality of the data, which are often violated in practical situations. We address the challenge of lack of training samples by creating two models to characterize the target class spectral variability --the first model makes no assumptions regarding inter-band correlation, while the second model uses a first-order Markovbased scheme to exploit correlation between bands. Using these models, we present two techniques for meeting these challenges-the kernel-based support vector data description (SVDD) and spectral fringe-adjusted joint transform correlation (SFJTC). We have developed an algorithm that uses the kernel-based SVDD for use in full-pixel target detection scenarios. We have addressed optimization of the SVDD kernel-width parameter using the golden-section search algorithm for unconstrained optimization. We investigated a proper number of signatures N to generate for the SVDD target class and found that only a small number of training samples is required relative to the dimensionality (number of bands). We have extended decision-level fusion techniques using the majority vote rule for the purpose of alleviating the problem of selecting a proper value of s 2 for either of our target variability models. We have shown that heavy spectral variability may cause SFJTC-based detection to suffer and have addressed this by developing an algorithm that selects an optimal combination of the discrete wavelet transform (DWT) coefficients of the signatures for use as features for detection. For most scenarios, our results show that our SVDD-based detection scheme provides low false positive rates while maintaining higher true positive rates than popular stochastic detection algorithms. Our results also show that our SFJTC-based detection scheme using the DWT coefficients can yield significant detection improvement compared to use of SFJTC using the original signatures and traditional stochastic and deterministic algorithms.
Author: Leidy P. Dorado-Munoz
Publisher:
ISBN:
Category : Multispectral imaging
Languages : en
Pages : 262
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Book Description
"Applications of optical remote sensing processes include environmental monitoring, military monitoring, meteorology, mapping, surveillance, etc. Many of these tasks include the detection of specific objects or materials, usually few or small, which are surrounded by other materials that clutter the scene and hide the relevant information. This target detection process has been boosted lately by the use of hyperspectral imagery (HSI) since its high spectral dimension provides more detailed spectral information that is desirable in data exploitation. Typical spectral target detectors rely on statistical or geometric models to characterize the spectral variability of the data. However, in many cases these parametric models do not fit well HSI data that impacts the detection performance. On the other hand, non-linear transformation methods, mainly based on manifold learning algorithms, have shown a potential use in HSI transformation, dimensionality reduction and classification. In target detection, non-linear transformation algorithms are used as preprocessing techniques that transform the data to a more suitable lower dimensional space, where the statistical or geometric detectors are applied. One of these non-linear manifold methods is the Schroedinger Eigenmaps (SE) algorithm that has been introduced as a technique for semi-supervised classification. The core tool of the SE algorithm is the Schroedinger operator that includes a potential term that encodes prior information about the materials present in a scene, and enables the embedding to be steered in some convenient directions in order to cluster similar pixels together. A completely novel target detection methodology based on SE algorithm is proposed for the first time in this thesis. The proposed methodology does not just include the transformation of the data to a lower dimensional space but also includes the definition of a detector that capitalizes on the theory behind SE. The fact that target pixels and those similar pixels are clustered in a predictable region of the low-dimensional representation is used to define a decision rule that allows one to identify target pixels over the rest of pixels in a given image. In addition, a knowledge propagation scheme is used to combine spectral and spatial information as a means to propagate the 'potential constraints' to nearby points. The propagation scheme is introduced to reinforce weak connections and improve the separability between most of the target pixels and the background. Experiments using different HSI data sets are carried out in order to test the proposed methodology. The assessment is performed from a quantitative and qualitative point of view, and by comparing the SE-based methodology against two other detection methodologies that use linear/non-linear algorithms as transformations and the well-known Adaptive Coherence/Cosine Estimator (ACE) detector. Overall results show that the SE-based detector outperforms the other two detection methodologies, which indicates the usefulness of the SE transformation in spectral target detection problems."--Abstract.
Author: H. Hanna Tran Haskett
Publisher:
ISBN:
Category :
Languages : en
Pages : 280
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Book Description
Author: Muhammad Faysal Islam
Publisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 140
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Book Description
