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检索条件"任意字段=Conference on Algorithms for Multispectral and Hyperspectral Imagery II"

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Anomaly detection in hyperspectral imagery: A comparison of methods using seasonal data

Anomaly detection in hyperspectral imagery: A comparison of ...

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XiiI

作者： Hytla, Patrick Hardie, Russell C. Eismann, Michael T. Meola, Joseph Univ Dayton Dept Elect & Comp Engn 300 Coll Pk Dr Dayton OH 45469 USA Air Force Res Lab Wright Patterson AFB OH 45433 USA

ISBN: (纸本)9780819466877

The use of hyperspectral imaging (HSI) technology to support a variety of civilian, commercial, and military remote sensing applications, is growing. The rich spectral information present in HSI allows for more accurate ground cover identification and classification than with panchromatic or multispectral imagery. One class of problems where hyperspectral images can be exploited, even when no a priori information about a particular ground cover class is available, is anomaly detection. Here spectral outliers (anomalies) are detected based on how well each hyperpixel (spectral irradiance vector for a given pixel position) fits within some background statistical model. Spectral anomalies may correspond to areas of interest in a given scene. In this work, we compare several anomaly detectors found in the literature in novel experiments. In particular, we study the performance of the anomaly detectors in detecting several man-made painted panels in a natural background using visible/near-infrared hyperspectral imagery. The data have been collected over the course of a nine month period, allowing us to test the robustness of the anomaly detectors with seasonal change. The detectors considered include the simple Gaussian anomaly detector, a Gaussian mixture model (GMM) anomaly detector, and the cluster-based anomaly detector (CBAD). We examine the effect of the number of components for the GMM and the number of clusters for the CBAD. Our preliminary results suggest that the use of a CBAD yields the best results for our data.

关键词： anomaly detection hyperspectral imagery Gaussian mixture model

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Detecting changes in scenes using autonomous smart cameras

Detecting changes in scenes using autonomous smart cameras

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XiiI

作者： Jaenisch, Holger M. Handley, James W. Hicklen, Michael L. Dtech Syst Inc POB 18924 Huntsville AL 35804 USA Axiom Corp Two piedmont Ctr Atlanta GA 30305 USA

ISBN: (纸本)9780819466877

This paper describes a novel smart camera algorithm capable of detecting important changes in scenes. These changes take the form of observed crowd dynamics, group behavior, and mounted and unmounted traffic flow. Using webcarn imagery of a football game, these cameras successfully exhibited scene understanding and detected anomalies without prior training or examples. Our algorithm and results are summarized in this paper.

关键词： smart camera webcam imagery anomaly detection scene understanding change detection crowd dynamics

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Random sampling statistical analysis for adaptive target-scale-invariant hyperspectral anomaly detection

Random sampling statistical analysis for adaptive target-sca...

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XiiI

作者： Romano, Joao M. Rosario, Dalton USA Armament Res & Dev Ctr Picatinny Arsenal NJ 07806 USA USA Res Lab Adelphi MD 20783 USA

ISBN: (纸本)9780819466877

Ground to ground, sensor to object viewing perspective presents a major challenge for autonomous window based object detection, since object scales at this viewing perspective cannot be approximated. In this paper, we present a fully autonomous parallel approach to address this challenge. Using hyperspectral (HS) imagery as input, the approach features a random sampling stage, which does not require secondary information (range) about the targets;a parallel process is introduced to mitigate the inclusion by chance of target samples into clutter background classes during random sampling;and a fusion of results. The probability of sampling targets by chance within the parallel processes is modeled by the binomial distribution family, which can assist on tradeoff decisions. Since this approach relies on the effectiveness of its core algorithmic detection technique, we also propose a compact test statistic for anomaly detection, which is based on a principle of indirect comparison. This detection technique has shown to preserve meaningful detections (genuine anomalies in the scene) while significantly reducing the number of false positives (e.g. transitions of background regions). To capture the influence of parametric changes using both the binomial distribution family and actual HS imagery.. we conducted a series of rigid statistical experiments and present the results in this paper.

关键词： parallel anomaly detection hyperspectral imagery and random sampling

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Beyond the adaptive matched filter: Nonlinear detectors for weak signals in high-dimensional clutter

Beyond the adaptive matched filter: Nonlinear detectors for ...

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XiiI

作者： Theiler, James Foy, Bernard R. Fraser, Andrew M. Los Alamos Natl Lab Space & Remote Sensing Sci Grp Los Alamos NM 87545 USA Los Alamos Natl Lab Phys Chem & Appl Spect Grp Los Alamos NM USA

ISBN: (纸本)9780819466877

For known signals that are linearly superimposed on gaussian backgrounds, the linear adaptive matched filter (AMF) is well-known to be the optimal detector. The AMF has furthermore proved to be remarkably effective in a broad range of circumstances where it is not optimal, and for which the optimal detector is not linear. In these cases. nonlinear detectors are theoretically superior, but direct estimation of nonlinear detectors in high-dimensional spaces often leads to flagrant overfitting and poor out-of-sample performance. Despite this difficulty in the general case, we will describe several situations in which nonlinearity can be effectively combined with the AMF to detect weak signals. This allows improvement over AMF performance while avoiding the full force of dimensionality's curse.

关键词： matched filter hyperspectral imagery adaptive signal detection

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Change detection for hyperspectral imagery

Change detection for hyperspectral imagery

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XiiI

作者： Vongsy, Karmon Karimkhan, Shamsaddin Shaw, Arnab K. Wicker, Devert Wright State Univ Dept Elect Engn Dayton OH 45435 USA Air Force Res Lab SNAT Wright Patterson AFB OH 45433 USA

ISBN: (纸本)9780819466877

Change Detection (CD) is the process of identifying temporal or spectral changes in signals or images. Detection and analysis of change provide valuable information of transformations in a scene. hyperspectral sensors provide spatial and spectrally rich information that can be exploited for Change Detection. This paper develops and analyzes various CD alaorithms for the detection of changes using single-pass and multi-pass hyperspectral images. For the validation and performance comparisons, changes obtained are compared for the conventional similarity correlation coefficient as well as traditional change detection algorithms, such as image differencing, image ratioing, and principle component analysis (PCA) methods. Another main objective is to incorporate Kernel based optimization by using a nonlinear mapping function. Development of nonlinear versions of linear algorithms allows exploiting nonlinear relationships present in the data. The nonlinear versions, however, become computationally intensive due to the high dimensionality of the feature space resulting in part from application of the nonlinear mapping function. This problem is overcome by implementing these nonlinear algorithms in the high-dimensional feature space in terms of kernels. Kernelization of a similarity correlation coefficient algorithm for hyperspectral change detection has been studied. Preliminary work on dismount tracking using change detection over successive HSI bands has shown promising results. CD between multipass HSI image cubes elicits the changes over time, whereas changes between spectral bands for the same cube illustrate the spectral changes occurring in different image regions, and results for both cases are given in the paper.

关键词： change detection hyperspectral image processing maximum likelihood change detection footprint detection vehicle and dismount detection

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hyperspectral image unmixing over benthic habitats

Hyperspectral image unmixing over benthic habitats

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XiiI

作者： Velez-Reyes, Miguel Rosario-Torres, Samuel Goodman, James A. Alvira, Enid M. Castrodad, Alexey Univ Puerto Rico Bernard M Gordon Ctr Subsurface Sensing & Imaging Lab Appl Remote Sensing & Image Proc POB 9048 Mayaguez PR 00681 USA

ISBN: (纸本)9780819466877

Benthic habitats are the different bottom environments as defined by distinct physical, geochemical, and biological characteristics. hyperspectral remote sensing has great potential to map and monitor the complex dynamics associated with estuarine and nearshore benthic habitats. However, utilizing hyperspectral unmixing to map these areas requires compensating for variable bathymetry and water optical properties. In this paper, we compare two methods to unmix hyperspectral imagery in estuarine and nearshore benthic habitats. The first method is a two-stage method where bathymetry and optical properties are first estimated using Lee's inversion model and linear unmixing is then performed using variable endmembers derived from propagating bottom spectral signatures to the surface using the estimated bathymetry and optical properties. In the second approach, a nonlinear optimization approach is used to simulatenously retrieve abundances, optical properties, and bathymetry. Preliminary results are presented using AVIRIS data from Kaneohe Bay, Hawaii. SHOALS data from the area is used to evaluate the accuracy of the retrieved bathymetry and comparisons between abundance estimates for sand, algae and coral are performed. These results show the potential of the nonlinear approach to provide better estimates of bottom coverage but at a significantly higher computational price. The experimental work also points to the need for a well characterized site to use for unmixing algorithms testing and validation.

关键词： hyperspectral imagery benthic habitat mapping unmixing bathymetry water optical properties

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Superresolution reconstruction of hyperspectral images

Superresolution reconstruction of hyperspectral images

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conference on Optical Pattern Recognition XViiI

作者： Elbakary, Mohamed Alam, Mohammed S. Univ S Alabama Dept Elect & Comp Engn Mobile AL 36688 USA

ISBN: (纸本)9780819466969

hyperspectral imagery is used for a wide variety of applications, including target detection, tacking, agricultural monitoring and natural resources exploration. The main reason for using hyperspectral imagery is that these images reveal spectral information about the scene that are not available in a single band. Unfortunately, many factors such as sensor noise and atmospheric scattering degrade the spatial quality of these images. Recently, many algorithms are introduced in the literature to improve the resolution of hyperspectral images [7]. In this paper, we propose a new method to produce high resolution bands from low resolution bands that are strongly correlated to the corresponding high resolution panchromatic image. The proposed method is based on using the local correlation instead of using the global correlation to improve the estimated interpolation in order to construct the high resolution image. The utilization of local correlation significantly improved the resolution of high resolution images when compared to the corresponding results obtained using the traditional algorithms. The local correlation is implemented by using predefined small windows across the low resolution image. In addition, numerous experiments are conducted to investigate the effect of the chosen window size in the image quality. Experiments results obtained using real life hyperspectral imagery is presented to verify the effectiveness of the proposed algorithm.

关键词： hyperspectral imagery multispectral imagery superresolution imaging resolution enhancement

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A comparative study of linear and nonlinear anomaly detectors for hyperspectral imagery

A comparative study of linear and nonlinear anomaly detector...

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XiiI

作者： Goldberg, Hirsh Nasrabadi, Nasser. M. Univ Maryland College Pk MD 20742 USA Army Res Lab ATTN AMSRD ARL SE SE Adelphi MD 20783 USA

ISBN: (纸本)9780819466877

In this paper we implement various linear and nonlinear subspace-based anomaly detectors for hyperspectral imagery. First. a dual window technique is used to separate the local area around each pixel into two regions - an inner-window region (IWR) and an outer-window region (OWR). Pixel spectra from each region are projected onto a subspace which is defined by projection bases that can be generated in several ways. Here we use three common pattern classification techniques (Principal Component Analysis (PCA), Fisher Linear Discriminant (FLD) Analysis, and the Eigenspace Separation Transform (EST)) to generate projection vectors. In addition to these three algorithms, the well-known Reed-Xiaoli (RX) anomaly detector is also implemented. Each of the four linear methods is then implicitly defined in a high- (possibly infinite-) dimensional feature space by using a nonlinear mapping associated with a kernel function. Using a common machine-learning technique known as the kernel trick all dot products in the feature space are replaced with a Mercer kernel function defined in terms of the original input data space. To determine how anomalous a given pixel is, we then project the current test pixel spectra and the spectral mean vector of the OWR onto the linear and nonlinear projection vecotrs in order to exploit the statistical differences between the IWR and OWR pixels. Anomalies are detected if the separation of the projection of the current test pixel spectra and the OWR mean spectra are greater than a certain threshold. Comparisons are made using receiver operating characteristics (ROC) curves.

关键词： anomaly detection hyperspectral imagery Eigenspace Separation Transform (EST) kernel-based machine learning kernel PCA kernel Fisher discriminant kernels

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Fast implementation of N-FINDR algorithm for endmember determination in hyperspectral imagery

Fast implementation of N-FINDR algorithm for endmember deter...

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XiiI

作者： Chowdhury, A. Alam, M. S. Univ S Alabama Dept Elect & Comp Engn Mobile AL 36688 USA

ISBN: (纸本)9780819466877

Analysis of hyperspectral imagery requires the extraction of certain basis spectra called endmembers, which are assumed to be the pure signatures in the image data. N-FINDR algorithm developed by Winter [1] is one of the most widely used technique for endmember extraction. This algorithm is based on the fact that in L spectral dimensions, the L-dimensional volume contained by a simplex formed from the purest pixels is larger than any other volume formed from other combination of pixels. Recently proposed algorithm based on virtual dimensionality (VD) determines the number of endmembers present in the dataset, where an endmember initialization algorithm (EIA) is used to select an appropriate set of pixels for initializing the N-FINDR process. In this paper, we proposed a fast algorithm to implement the N-FINDR technique which has much better computational efficiency than the existing techniques. In the proposed technique, we used the VD to find the number of endmembers N. Then we reduced the dimensionality of the hyperspectral dataset to N-1 by using the principal component transformation (PCT) and divided all the pixels into N number of classes by using the spectral angle map (SAM). We extracted N number of the most pure pixels from each group by using the classical N-FINDR algorithm but with exhaustive search. Thus we get N-2 pixels that are most likely to be the actual endmembers. The classical N-FINDR algorithm is then again applied on these selected pixels to find the final N endmember. Grouping the pixels into several classes makes the computation very fast. Since we extracted N number of pixels from each group by exhaustive search, there is no possibility of loosing any endmember due to classification.

关键词： hyperspectral imaging Algorithm development Computer simulations Data modeling Binary data Image analysis 3D modeling Computer engineering Detection and tracking algorithms Image processing

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Visualization of hyper spectral imagery

Visualization of hyper spectral imagery

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XiiI

作者： Hogervorst, Maarten A. Bijl, Piet Toet, Alexander TNO Human Factors POB 23 NL-3769 ZG Soesterberg Netherlands

ISBN: (纸本)9780819466877

We developed four new techniques to visualize hyper spectral image data for man-in-the-loop target detection. The methods respectively: (1) display the subsequent bands as a movie ("movie"), (2) map the data onto three channels and display these as a colour image ("colour"), (3) display the correlation between the pixel signatures and a known target signature ("match") and (4) display the output of a standard anomaly detector ("anomaly"). The movie technique requires no assumptions about the target signature and involves no information loss. The colour technique produces a single image that can be displayed in real-time. A disadvantage of this technique is loss of information. A display of the match between a target signature and pixels and can be interpreted easily and fast, but this technique relies on precise knowledge of the target signature. The anomaly detector signifies pixels with signatures that deviate from the (local) background. We performed a target detection experiment with human observers to determine their relative performance with the four techniques,. The results show that the "match" presentation yields the best performance, followed by ''movie" and "anomaly", while performance with the "colour" presentation was the poorest. Each scheme has its advantages and disadvantages and is more or less suited for real-time and post-hoc processing. The rationale is that the final interpretation is best done by a human observer. In contrast to automatic target recognition systems, the interpretation of hyper spectral imagery by the human visual system is robust to noise and image transformations and requires a minimal number of assumptions (about signature of target and background, target shape etc.) When more knowledge about target and background is available this may be used to help the observer interpreting the data (aided target detection).

关键词： target acquisition detection colour noise multi-band human observer

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