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Effects of Random Measurements on the Performance of Target Detection in hyperspectral imagery

Effects of Random Measurements on the Performance of Target ...

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

作者： Chen, Yi Nasrabadi, Nasser M. Tran, Trac D. Johns Hopkins Univ Dept Elect & Comp Engn 3400 N Charles St Baltimore MD 21218 USA US Army Res Lab Adelphi MD 20783 USA

ISBN: (纸本)9780819486226

hyperspectral pixels are acquired in hundreds of narrow and continuous spectral bands, and the hyperspectral data cubes typically contain hundreds of megabytes. Analysis and processing of the high-dimensional hyperspectral data are computationally expensive and memory inefficient. However, there is a large amount of redundancy between neighboring spectral bands and the hyperspectral pixels lie in a much lower dimensional subspace. Therefore, numerous techniques can be applied to reduce the dimensionality while maintaining the structure of the data. This would lead to a significant reduction of the complexity of the imaging system, as well as an improvement of the computational efficiency of the detection algorithms. In this paper, we explore the use of several dimensionality reduction techniques that can be easily integrated into the imaging sensors. We also investigate their effect on the performance of classical target detection techniques for hyperspectral images, including spectral matched filters (SMF), matched subspace detectors (MSD), support vector machines (SVM), and RX anomaly detection algorithm. Specifically, each N-dimensional spectral pixel is embedded to an M-dimensional measurement space with M << N by a linear transformation (e.g., random measurement matrices, uniform downsampling, PCA). The SMF, MSD, SVM, and RX detectors are then applied to the M-dimensional measurement vectors to detect the targets of interests and their detection performances are compared to those obtained from the entire N-dimensional spectrum by the receiver operating characteristics curves. Through extensive experiments on several HSI datasets, we demonstrate that only 1/5 to 1/3 measurements (i.e., the compression ratio M/N is 1/5 similar to 1/3) are necessary to achieve detection performance comparable to that obtained by exploiting the full N-dimensional pixels.

关键词： Single mode fibers Target detection Sensors Matrices Principal component analysis Detection and tracking algorithms hyperspectral imaging hyperspectral target detection Optical sensors Optical filters

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Multi-Resolution and Directional Filtering Techniques for Detecting Dust Storm Direction in Satellite imagery

Multi-Resolution and Directional Filtering Techniques for De...

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

作者： Alkhatib, Mohammed Q. Cabrera, Sergio D. Univ Texas El Paso Dept Elect & Comp Engn El Paso TX 79968 USA

ISBN: (纸本)9780819486226

This paper presents a new method for finding the direction of a dust storm in satellite images including the 5-band NOAA-AVHRR imagery that were used in our previous work. The previous methods for obtaining the prominent direction of the dust storms involved the combination of edge detectors and local spectral-domain classification techniques applied to subimages/blocks. These approaches produced promising results but have the limitation of not providing consistent results among the subimages that overlap the dust storm region. In this paper, other algorithms like wavelets and state-of-the-art directional filters, based on the contourlet transform, are used to help us determine the direction with more precision and consistency among the relevant subimages. Before applying the directional filtering to the candidate region of the multispectral image, a preprocessing step involves passing the image through a nonsubsampled pyramid selective amplification, this preprocessing step is required in order to enhance the image and improve its directional streaks, in turn, this will help improve the performance of the directional filter to get better and more consistent results. For AVHRR images, our methodology involves applying directional filtering on bands 4 or 5 since these wavelengths highlight the absorption and subsequent emission of thermal radiation by the silicate particles in the dust storms. Directional filtering is applied on these image bands at different angles where energy measurements are computed to find the prominent direction of the dust storm. The presence of a prominent direction in the texture of the candidate region of the dust storm can be used as a verification of its presence in an automated detection system.

关键词： multispectral imagery NOAA-AVHRR imagery dust storm detection image enhancement multiresolution analysis directional filtering contourlet transform

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Interactive visualization of hyperspectral images on a hyperbolic disk

Interactive visualization of hyperspectral images on a hyper...

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

作者： Goodenough, Adam A. Schlamm, Ariel Brown, Scott D. Messinger, David Rochester Inst Technol Rochester NY 14623 USA

ISBN: (纸本)9780819486226

Visualization of the high-dimensional data set that makes up hyperspectral images necessitates a dimensionality reduction approach to make that data useful to a human analyst. The expression of spectral data as color images, individual pixel spectra plots, principal component images, and 2D/3D scatter plots of a subset of the data are a few examples of common techniques. However, these approaches leave the user with little ability to intuit knowledge of the full N-dimensional spectral data space or to directly or easily interact with that data. In this work, we look at developing an interactive, intuitive visualization and analysis tool based on using a Poincare disk as a window into that high dimensional space. The Poincare disk represents an infinite, two-dimensional hyperbolic space such that distances and areas increase exponentially as you move farther from the center of the disk. By projecting N-dimensional data into this space using a non-linear, yet relative distance metric preserving projection (such as the Sammon projection), we can simultaneously view the entire data set while maintaining natural clustering and spacing. The disk also provides a means to interact with the data;the user is presented with a "fish-eye" view of the space which can be navigated and manipulated with a mouse to "zoom" into clusters of data and to select spectral data points. By coupling this interaction with a synchronous view of the data as a spatial RGB image and the ability to examine individual pixel spectra, the user has full control over the data set for classification, analysis, and instructive use.

关键词： hyperspectral visualization hyperbolic projection interactive classification imagery dimension reduction

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Automatic clustering of multispectral imagery by maximization of the graph modularity

Automatic clustering of multispectral imagery by maximizatio...

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

作者： Mercovich, Ryan A. Harkin, Anthony Messinger, David Rochester Inst Technol Ctr Imaging Sci Rochester NY 14623 USA Sch Math Sci Rochester Inst Technol Rochester NY 14623 USA

ISBN: (纸本)9780819486226

Automatic clustering of spectral image data is a common problem with a diverse set of desired and potential solutions. While typical clustering techniques use first order statistics and Gaussian models, the method described in this paper utilizes the spectral data structure to generate a graph representation of the image and then clusters the data by applying the method of optimal modularity for finding communities within the graph. After defining and identifying pixel adjacencies to represent an image as an adjacency matrix, a recursive splitting is performed to group spectrally similar pixels using the method of modularity maximization. The careful selection of pixel adjacencies determines the success of this spectral clustering technique. The modularity maximization process uses the eigenvector of the modularity matrix with the largest positive eigenvalue to split groups of pixels with non-linear decision surfaces and uses the modularity measure to help estimate the optimal number of clusters to best characterize the data. Using information from each recursion, the end result is a variable level of detail cluster map that is more visually useful than previous methods. Additionally, this method outperforms many typical automatic clustering methods such k-means, especially in highly cluttered urban scenes. The optimal modularity technique hierarchically clusters spectral image data and produces results that more reliably characterize the number of clusters in the data than common automatic spectral image clustering techniques.

关键词： clustering modularity unsupervised classification multispectral image processing graph theory

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A scalable hierarchical approach for leveraging low resolution imagery for image classification

A scalable hierarchical approach for leveraging low resoluti...

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

作者： Padula, Francis Gross, Harry Munechika, Curt Pogorzala, David Integr Applicat Inc Chantilly VA 20151 USA

ISBN: (纸本)9780819486226

The current extent of publicly available space-based imagery and data products is unprecedented. Data from research missions and operational environmental programs provide a wealth of information to global users, and in many cases, the data are accessible in near real-time. The availability of such data provides a unique opportunity to investigate how information can be cascaded through multiple spatial, spectral, radiometric, and temporal scales. A hierarchical image classification approach is developed using multispectral data sources to rapidly produce large area landuse identification and change detection products. The approach derives training pixels from a coarser resolution classification product to autonomously develop a classification map at improved resolution. The methodology also accommodates parallel processing to facilitate analysis of large amounts of data. Previous work successfully demonstrated this approach using a global MODIS 500 m landuse product to construct a 30 m Landsat-based classification map. This effort extends the previous approach to high resolution U.S. commercial satellite imagery. An initial validation study is performed to document the performance of the algorithm and identify limitations in the process. Results indicate this approach is scalable and has broad applications to target and anomaly detection applications. In addition, discussion is focused on how information is preserved throughout the processing chain, as well as situations where the data integrity could break down. This work is part of a larger effort to deduce practical, innovative, and alternative ways to leverage and exploit the extensive low-resolution global data archives to address relevant civil, environmental, and defense objectives.

关键词： Image classification sensor fusion hierarchical low-resolution data

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Estimating True Color imagery for GOES-R

Estimating True Color Imagery for GOES-R

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

作者： Grossberg, Michael D. Shahriar, Fazlul Gladkova, Irina Alabi, Paul K. Hillger, Donald W. Miller, Steven D. CUNY City Coll 160 Convent Ave New York NY 10031 USA CUNY Grad Ctr New York NY 10016 USA NOAA NESDIS Regional and Mesoscale Meteorology Branch Ft Collins CO USA Cooperat Inst Res Atmosphere Ft Collins CO USA

ISBN: (纸本)9780819486226

The Advanced Baseline Imager (ABI) on GOES-R will help NOAA's objective of engaging and educating the public on environmental issues by providing near real-time imagery of the earth-atmosphere system. True color satellite images are beneficial to the public, as well as to scientists, who use these images as an important "decision aid" and visualization tool. Unfortunately, ABI only has two visible bands (cyan and red) and does not directly produce the three bands (blue, green, and red) used to create true color imagery. We have developed an algorithm that will produce quantitative true color imagery from ABI. Our algorithm estimates the three tristimulus values of the international standard CIE 1931 XYZ colorspace for each pixel of the ABI image, and thus is compatible with a wide range of software packages and hardware devices. Our algorithm is based on a non-linear statistical regression framework that incorporate both classification and local multispectral regression using training data. We have used training data from the hyper-spectral imager Hyperion. Our algorithm to produce true color images from the ABI is not specific to ABI and may be applicable to other satellites which, like the ABI, do not have the ability to directly produce RGB imagery.

关键词： GOES-R ABI CIE-XYZ Hyperion green color regression true color

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Demonstration of delivery of ortho imagery in realtime for local emergency response

Demonstration of delivery of ortho imagery in realtime for l...

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

作者： McKeown, Donald Faulring, Jason Krzaczek, Robert Cavilia, Stephen van Aardt, Jan Rochester Inst Technol Digital Imaging & Remote Sensing Lab Chester F Carlson Ctr Imaging Sci Rochester NY 14623 USA

ISBN: (纸本)9780819486226

The Information Products Laboratory for Emergency Response (IPLER) is a new initiative led by the Rochester Institute of Technology (RIT) to develop and put into use new information products and tools derived from remote sensing data. This effort involves technical development and outreach to the user community having the two-fold objective of providing new information tools to enhance public safety and fostering economic development. Specifically, this paper addresses the demonstration of the collection and delivery of geo-referenced overhead imagery to local (county level) emergency managers in near realtime. The demonstration proved valuable to county personnel in showing what is possible and valuable to the researchers in highlighting the very real constraints of operatives in local government. The demonstration consisted of four major elements;1) a multiband imaging system incorporating 4 cameras operating simultaneously in the visible (color), shortwave infrared, midwave infrared and long wave infrared, 2) an on-board inertial navigation and data processing system that renders the imagery into geo-referenced coordinates, 3) a microwave digital downlink, and 4) a data dissemination service via FTP and WMS-based browser. In this particular exercise, we successfully collected and downloaded over 700 images and delivered them to county servers located in their Emergency Operations Center as well as to a remote GIS van.

关键词： remote sensing information products disaster response emergency management

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hyperspectral object tracking using small sample size

Hyperspectral object tracking using small sample size

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

作者： Rosario, Dalton Kling, Henry USA Res Lab 2800 Powder Mill Rd Adelphi MD 20783 USA US Army Night Vis & Elect Sensors Directorate Ft Belvoir VA 22060 USA

ISBN: (纸本)9780819481597

This paper introduces a simple approach for object tracking using hyperspectral (HS) spectral features. The approach addresses the object tracking problem using a small object sample size. For a particular application, the key challenges are: (i) Offline training cannot be utilized;(ii) motor vehicles of interest (targets) have a small sample size (e. g., less than 9);and (iii) kinematic states of targets cannot be used for tracking, since stationary targets are also of interest. Using HS imagery, this paper introduces a method that exploits the mean and median averages spectra to estimate higher moments of the underlying (and unknown) probability distribution function of spectra;in particular, skew tendency and sign. Tracking HS targets is then possible using this algorithm to test a sequence of HS imagery, given that target spectra are initially cued by the user. The approach was implemented into a commercially off the shelf workstation, featuring the IBM Cell Processor and GA-180 Add in Board. Preliminary results are promising using a challenging HS data cube.

关键词： Target tracking hyperspectral imagery IBM Cell Processor

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hyperspectral Target Detection Assessment Using An Online Evaluation System

Hyperspectral Target Detection Assessment Using An Online Ev...

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

作者： Zhu, Wei Du, Qian Mississippi State Univ Dept Elect & Comp Engn Mississippi State MS 39762 USA

ISBN: (纸本)9780819481597

In this paper, we evaluate several popular and simple matched-filter-based target detection algorithms using the online accessible HyMap data and ground truth provided by Digital Imaging and Remote Sensing (DIRS) Laboratory at Rochester Institute of Technology, and compare with their regularized versions. Based on both self and blind tests, the performance of these widely used detectors is objectively and fairly compared. Based on our experience, some recommendations for possible system improvements are provided.

关键词： hyperspectral imagery Target Detection

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Evaluation of Different Structural Models for Target Detection in hyperspectral imagery

Evaluation of Different Structural Models for Target Detecti...

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

作者： Pena-Ortega, Carolina Velez-Reyes, Miguel Univ Puerto Rico Lab Appl Remote Sensing & Image Proc Dept Elect & Comp Engn Mayaguez PR 00681 USA

ISBN: (纸本)9780819481597

Target detection is an essential component for defense, security and medical applications of hyperspectral imagery. Structured and unstructured models are used to model variability of spectral signatures, for the design of information extraction algorithms. In structured models, spectral variability is modeled using different geometric representations. In linear approaches, the spectral signatures are assumed to be generated by the linear combination of basis vectors. The nature of the basis vectors, and its allowable linear combinations, define different structural models such as vector subspaces, polyhedral cones, and convex hulls. In this paper, we investigate the use of these models to describe background of hyperspectral images, and study the performance of target detection algorithms based on these models. We also study the effect of the model order in the performance of target detection algorithms based on these models. Results show that model order is critical to algorithm performance. Underfitting or overfitting result in poor performance. Models based on subspace are of lower order than those based on polyhedral cones or convex hulls. With good target to background contrast all models perform well.

关键词： hyperspectral imagery Target detection Structured models Endmember extraction

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