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Author: Adam Cisz
Publisher:
ISBN:
Category : Computer algorithms
Languages : en
Pages : 262
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Book Description
"This thesis performs a performance comparison on existing hyperspectral target detection algorithms. The algorithms chosen for this analysis include multiple adaptive matched filters and the physics based modeling invariant technique. The adaptive matched filter algorithms can be divided into either structured (geometrical) or unstructured (statistical) algorithms. The difference between these two categories is in the manner in which the background is characterized. The target detection procedure includes multiple pre-processing steps that are examined here as well. The effects of atmospheric compensation, dimensionality reduction, background characterization, and target subspace creation are all analyzed in terms of target detection performance. At each step of the process, techniques were chosen that consistently improved target detection performance. The best case scenario for each algorithm is used in the final comparison of performance. The results for multiple targets were computed and statistical matched filter algorithms were shown to outperform all others in a fair comparison. This fair comparison utilized a FLAASH atmospheric compensation for the matched filters that was equivalent to the physics based invariant process. The invariant technique was shown to outperform the geometric matched filters that it uses in its approach. Each of these techniques showed improvement over the SAM algorithm for three of the four targets analyzed. Multiple theories are proposed to explain the anomalous results for the most difficult target"--Abstract.
Author: Adam Cisz
Publisher:
ISBN:
Category : Computer algorithms
Languages : en
Pages : 262
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Book Description
"This thesis performs a performance comparison on existing hyperspectral target detection algorithms. The algorithms chosen for this analysis include multiple adaptive matched filters and the physics based modeling invariant technique. The adaptive matched filter algorithms can be divided into either structured (geometrical) or unstructured (statistical) algorithms. The difference between these two categories is in the manner in which the background is characterized. The target detection procedure includes multiple pre-processing steps that are examined here as well. The effects of atmospheric compensation, dimensionality reduction, background characterization, and target subspace creation are all analyzed in terms of target detection performance. At each step of the process, techniques were chosen that consistently improved target detection performance. The best case scenario for each algorithm is used in the final comparison of performance. The results for multiple targets were computed and statistical matched filter algorithms were shown to outperform all others in a fair comparison. This fair comparison utilized a FLAASH atmospheric compensation for the matched filters that was equivalent to the physics based invariant process. The invariant technique was shown to outperform the geometric matched filters that it uses in its approach. Each of these techniques showed improvement over the SAM algorithm for three of the four targets analyzed. Multiple theories are proposed to explain the anomalous results for the most difficult target"--Abstract.
Author: Jason T. Casey
Publisher:
ISBN:
Category : Computer algorithms
Languages : en
Pages : 138
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Book Description
"Line scanning hyperspectral imaging systems are capable of capturing accurate spatial and spectral information about a scene. These data can be useful for detecting sub-pixel targets. Such systems, however, may be limited by certain key characteristics in their design. Systems employing multiple spectrometers, or that collect data from multiple focal planes may suffer an inherent misregistration between sets of collected spectral bands. In order to utilize the full spectrum for target detection purposes, the sets of bands must be registered to each other as precisely as possible. Perfect registration is not possible, due to both the sensor design, and variation in sensor orientation during data acquisition. The issue can cause degradation in the performance of various target detection algorithms. An analysis of algorithms is necessary to determine which perform well when working with misregistered data. In addition, new algorithms may need to be developed which are more robust in these conditions. The work set forth in this thesis will improve the registration between spectral bands in a line scanning hyperspectral sensor by using a geometric model of the sensor along with aircraft orientation parameters to pair sets of image pixels based on their ground locations. Synthetic scenes were created and band-to-band misregistration was induced between the VIS and NIR spectral channels to test the performance of various hyperspectral target detection algorithms when applied to misregistered hyperspectral data. The results for this case studied show geometric algorithms perform well using only the VIS portion of the EM spectrum, and do not always benefit from the addition of NIR bands, even for small amounts of misregistration. Stochastic algorithms appear to be more robust than geometric algorithms for datasets with band-to-band misregistration. The stochastic algorithms tested often benefit from the addition of NIR bands, even for large amounts of misregistration."--Abstract.
Author: David C. Grimm
Publisher:
ISBN:
Category : Computer algorithms
Languages : en
Pages : 119
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Book Description
"Detection of a known target in an image has several different approaches. The complexity and number of steps involved in the target detection process makes a comparison of the different possible algorithm chains desirable. Of the different setps involved, some have a more significant impact than others on the final result - the ability to find a target in an image. These more important steps often include atmospheric compensation, noise and dimensionality reduction, background characterization, and detection (matched filtering for this research). A brief overview of the algorithms to be compared for each step will be presented. This research seeks to identify the most effective set of algorithms for detecting a known target. Several different algorithms for each step will be presented, to include ELM, FLAASH, ACORN, MNF, PPI, N-FINDR, MAXD, and two matched filters that employ a structured background model - OSP and ASD. The chains generated by these algorithms will be compared using the Forest Radiance I HYDICE data set. Finally, ROC curves and AFAR values are calculated for each algorithm chain and a comparison of them is presented. Detection rates at a CFAR are also compared. Since a relatively small number of algorithms were used for each step, there were no definitive results generated. However, a comprehensive comparison of the chains using the above mentioned algorithms is presented"--Abstract.
Author:
Publisher:
ISBN:
Category : Algorithms
Languages : en
Pages : 71
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Book Description
The motivation of this work is to investigate the use of data clustering to improve our ability to detect targets within hyperspectral images. Target detection algorithms operate by identifying locations that are likely to contain a target when compared with the background. We propose a new clustering-based target detection method that allows multiple background models to be used. This new method pairs a clustering algorithm with an array of spectral matched filters. We then analyze the performance of various clustering algorithms when used with this method to detect targets in aerial hyperspectral images. We evaluate the performance of our clustered target detector on several aerial hyperspectral images when using clusters generated by several popular algorithms, specifically k-means, spectral clustering, Gaussian mixture models, and two variants of subspace clustering. We show empirically that our tuned algorithm outperforms all others when used for this task, outpacing the traditional Gaussian mixture model with a pAUC score of 0.219 for the same case above, thereby offering over a 14-fold improvement in performance. We offer several hypotheses to explain these results. We then discuss some of the features, most notably the versatility provided by the regularizer, that make the tuned LapGMM algorithm well suited for this application. Considering future work, we propose a number of potential applications for our tuned LapGMM algorithm, as well as several potential improvements or modifications to the clustered target detector that may be worth further investigation.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 389
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Book Description
This research extends the field of hyperspectral target detection by developing autonomous anomaly detection and signature matching methodologies that reduce false alarms relative to existing benchmark detectors. The proposed anomaly detection methodology adapts multivariate outlier detection algorithms for use with hyperspectral datasets containing thousands of high-dimensional spectral signatures. In so doing, the limitations of existing, non-robust anomaly detectors are identified, an autonomous clustering methodology is developed to divide an image into homogeneous background materials, and competing multivariate outlier detection methods are evaluated. To arrive at a final detection algorithm, robust parameter design methods are employed to determine parameter settings that achieve good detection performance over a range of hyperspectral images and targets. The final anomaly detection algorithm is tested against existing local and global anomaly detectors, and is shown to achieve superior detection accuracy when applied to a diverse set of hyperspectral images. The proposed signature matching methodology employs image-based atmospheric correction techniques in an automated process to transform a target reflectance signature library into a set of image signatures. This set of signatures is combined with an existing linear filter to form a target detector that is shown to perform as well or better relative to detectors that rely on complicated, information-intensive atmospheric correction schemes. The performance of the proposed methodology is assessed using a range of target materials in both woodland and desert hyperspectral scenes.
Author: Saurabh Prasad
Publisher: Springer Nature
ISBN: 3030386171
Category : Computers
Languages : en
Pages : 464
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Book Description
This book reviews the state of the art in algorithmic approaches addressing the practical challenges that arise with hyperspectral image analysis tasks, with a focus on emerging trends in machine learning and image processing/understanding. It presents advances in deep learning, multiple instance learning, sparse representation based learning, low-dimensional manifold models, anomalous change detection, target recognition, sensor fusion and super-resolution for robust multispectral and hyperspectral image understanding. It presents research from leading international experts who have made foundational contributions in these areas. The book covers a diverse array of applications of multispectral/hyperspectral imagery in the context of these algorithms, including remote sensing, face recognition and biomedicine. This book would be particularly beneficial to graduate students and researchers who are taking advanced courses in (or are working in) the areas of image analysis, machine learning and remote sensing with multi-channel optical imagery. Researchers and professionals in academia and industry working in areas such as electrical engineering, civil and environmental engineering, geosciences and biomedical image processing, who work with multi-channel optical data will find this book useful.
Author: Chein-I Chang
Publisher: Springer Science & Business Media
ISBN: 1441991700
Category : Computers
Languages : en
Pages : 372
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Book Description
Hyperspectral Imaging: Techniques for Spectral Detection and Classification is an outgrowth of the research conducted over the years in the Remote Sensing Signal and Image Processing Laboratory (RSSIPL) at the University of Maryland, Baltimore County. It explores applications of statistical signal processing to hyperspectral imaging and further develops non-literal (spectral) techniques for subpixel detection and mixed pixel classification. This text is the first of its kind on the topic and can be considered a recipe book offering various techniques for hyperspectral data exploitation. In particular, some known techniques, such as OSP (Orthogonal Subspace Projection) and CEM (Constrained Energy Minimization) that were previously developed in the RSSIPL, are discussed in great detail. This book is self-contained and can serve as a valuable and useful reference for researchers in academia and practitioners in government and industry.
Author:
Publisher:
ISBN:
Category : Computer algorithms
Languages : en
Pages : 804
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Book Description
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: George Azzopardi
Publisher: Springer
ISBN: 3319231170
Category : Computers
Languages : en
Pages : 821
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Book Description
The two volume set LNCS 9256 and 9257 constitutes the refereed proceedings of the 16th International Conference on Computer Analysis of Images and Patterns, CAIP 2015, held in Valletta, Malta, in September 2015. The 138 papers presented were carefully reviewed and selected from numerous submissions. CAIP 2015 is the sixteenth in the CAIP series of biennial international conferences devoted to all aspects of computer vision, image analysis and processing, pattern recognition, and related fields.
