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18th Annual conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery

ISBN: (纸本)9780819490681

The proceedings contain 85 papers. The topics discussed include: confusion and clairvoyance: some remarks on the composite hypothesis testing problem;hyperspectral anomaly detection based on variable dimension for clutter subspace;parameter estimation for support vector anomaly detection in hyperspectral imagery;leveraging lidar data to aid in hyperspectral image target detection in the radiance domain;an automatic detection software for differential reflection spectroscopy;a multi-temporal analysis approach for land cover mapping in support of nuclear incident response;mid-wave IFTS measurements of a laboratory-scale laminar flame;parking lot process model incorporated into DIRSIG scene simulation;using WorldView-2 vis-NIR multispectral imagery to support land mapping and feature extraction using normalized difference index ratios;a LWIR hyperspectral imager using a Sagnac interferometer and cooled HgCdTe detector array;and combination of a dispersive spectral imager and of a static Fourier transform spectral imager: study of concept.

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Vehicle Detection in WorldView-2 Satellite imagery Based on Gaussian Modeling and Contextual Learning

Vehicle Detection in WorldView-2 Satellite Imagery Based on ...

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Annual conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XViiI

作者： Shen, Bichuan Chen, Chi-Hau Marchisio, Giovanni B. Adv Micro Devices Inc 90 Cent St Boxboro MA 01719 USA Univ Massachusetts Dartmouth Dept Elect & Comp Engn N Dartmouth MA 02747 USA Digital Globe Longmont CO 80503 USA

ISBN: (纸本)9780819490681

In this paper, we aim to study the detection of vehicles from WorldView-2 satellite imagery. For this purpose, accurate modeling of vehicle features and signatures and efficient learning of vehicle hypotheses are critical. We present a joint Gaussian and maximum likelihood based modeling and machine learning approach using SVM and neural network algorithms to describe the local appearance densities and classify vehicles from non-vehicle buildings, objects, and backgrounds. Vehicle hypotheses are fitted by elliptical Gaussians and the bottom-up features are grouped by Gabor orientation filtering based on multi-scale analysis and distance transform. Global contextual information such as road networks and vehicle distributions can be used to enhance the recognition. In consideration of the problem complexity the practical vehicle detection task faces due to dense and overlapping vehicle distributions, partial occlusion and clutters by building, shadows, and trees, we employ a spectral clustering strategy jointly combined with bootstrapped learning to estimate the parameters of centroid, orientation, and extents for local densities. We demonstrate a high detection rate 94.8%, with a missing rate 5.2% and a false alarm rate 5.3% on the WorldView-2 satellite imagery. Experimental results show that our method is quite effective to model and detect vehicles.

关键词： WorldView-2 imagery vehicle detection Gaussian modeling contextual learning spectral clustering

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Feature Extraction, Anomaly, and Change Detection on WorldView 2 imagery by Hierarchical Image Segmentation- a study

Feature Extraction, Anomaly, and Change Detection on WorldVi...

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Annual conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XViiI

作者： Prasad, Lakshman Theiler, James Fair, Matthew Swaminarayan, Sriram Los Alamos Natl Lab Los Alamos NM 87545 USA

ISBN: (纸本)9780819490681

We study spatio-spectral feature extraction and image-adaptive anomaly and change detection on 8-band WorldView 2 imagery using a hierarchical polygonal image segmentation scheme. Features are represented as polygons with spectral and structural attributes, along with neighborhood structure and containment hierarchy for contextual feature identification. Further, the hierarchical segmentation provides multiple, coarse-scale, sub-backgrounds representing relatively uniform regions, which localize and simplify the spectral distribution of an image. This paves the way for facilitating anomaly and change detection when restricted to the contexts of these backgrounds. For example, forestry, urban areas, and agricultural land have very different spatio-spectral characteristics and their joint contribution to the image statistics can result in a complex distribution against which detecting anomalies could in general be a challenging problem. Our segmentation scheme provides sub-regions in the later stages of the hierarchy that correspond to homogeneous areas of an image while at the same time allowing inclusion of distinctive small features embedded in these regions. The exclusion of other image areas by focusing on these sub-backgrounds helps discover these outliers more easily with simpler methods of discrimination. By selecting appropriate bands in WorldView2 imagery, the above approach can be used to achieve fine spatio-spectral control in searching and characterizing features, anomalies, and changes of interest. The anomalies and changes are also polygons, which have spectral and structural attributes associated with them, allowing further characterization in the larger context of the image. The segmentation and feature detections can be used as multiple layers in a Geospatial Information System (GIS) for annotating imagery.

关键词： Image Segmentation Feature Extraction Change Detection Anomaly Hierarchy

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Improvement in the detection of land cover classes using the worldview-2 imagery

Improvement in the detection of land cover classes using the...

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American Society for Photogrammetry and Remote Sensing Annual conference 2012, ASPRS 2012

作者： Elsharkawy, Ahmed Elhabiby, Mohamed El-Sheimy, Naser Dept. of Geomatics Engineering University of Calgary Calgary AB T2N 1N4 Canada Public Works Department Faculty of Engineering Ain Shams University Cairo Egypt

ISBN: (纸本)9781622764068

Recent advances in satellite and airborne sensors make spatial and multispectral high-resolution imagery effortlessly available. These advances give the chance to address and solve some old problems related to the poor spatial or spectral resolution;such as the lack of details for certain features or the inability of the traditional classifiers to detect some land cover types due to the missing of important parts of the spectrum. High-resolution imagery is particularly well suited to urban applications. Previous High-resolution imagery data sources (such as IKONOS or QuickBird) did show the required spatial details necessary to provide a good urban planning solution, but they fail to represent a reasonable spectral resolution, as they only contain three visible bands and one near infra red band, which hinders the ability of traditional spectral classifiers to accurately detect some land-cover types. The increased spatial information in sub-meter imagery accompanied with a low spectral resolution make the process of image classification using traditional supervised or unsupervised spectral classification algorithms a very difficult task. The previous limitations led many researchers to the object-oriented solution to provide a reliable classification results. This study evaluated how spectral and spatial resolution of the new satellite World View-2 influences the quality of the classification using only traditional spectral classifiers which mainly depends on the information provided by the 8-spectral bands ranging from the visible to mid-infra red bands, investigating the rule of the new bands in accurately distinguishing detailed land cover types. This study assesses the accuracy of the classification for a data set of WorldView-2 satellite imagery when using the full 8-bands (data set I), and when using only the three-visible bands and the NIR band only (data set ii). The study uses the Maximum Likelihood Classifier method for extracting land cover information. Al

关键词： Spectral resolution

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Parameter-Free K-Means Clustering Algorithm for Satellite imagery Application

Parameter-Free K-Means Clustering Algorithm for Satellite Im...

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International conference on Information Science and Applications (ICISA)

作者： Kitti Koonsanit Chuleerat Jaruskulchai Apisit Eiumnoh Department of Computer Science Faculty of Science Kasetsart University Thailand National Center for Genetic Engineering and Biotechnology NSTDA Thailand

Unsupervised classification is a popular tool for unlabeled datasets in data mining and exploratory data analysis, such as K-means and Fuzzy C-mean. Although these unsupervised techniques have demonstrated substantial success for satellite imagery, they have some limitations. The initialization number of clusters in K-means clustering application is often needed in advance as an input parameter to the algorithm. Our previous paper regarding the initialization number of clusters in K-means clustering application with a co-occurrence matrix technique has been published. Although our previous approach regarding the number of cluster was discovered, but it was limited to count a number of peaks in occurrence matrix as the number of clusters by manual counting. The best of our previous approach need to automatically find and count a number of peaks in occurrence matrix. In this research, we assume that the satellite imagery is given and we have no knowledge beforehand for segmentation. Hence, this paper presents a simple, parameter-free K-means method for K-means in satellite imagery clustering application to determine the initialization number of clusters with image processing algorithms based on the co-occurrence matrix technique. A maxima technique is proposed for automatic counting a number of peaks in occurrence matrix as the number of clusters. The parameter-free method was tested with hyperspectral imagery and multispectral imagery. The results from the tests confirm the effectiveness of the proposed method in K-means method and compared with isodata algorithm.

关键词： Clustering algorithms Satellites Algorithm design and analysis Classification algorithms hyperspectral imaging

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Parameter-free K-means clustering algorithm for satellite imagery application

2012 International Conference on Information Science and App...

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2012 International conference on Information Science and Applications, ICISA 2012 2012年

作者： Koonsanit, Kitti Jaruskulchai, Chuleerat Eiumnoh, Apisit Department of Computer Science Faculty of Science Kasetsart University Thailand National Center for Genetic Engineering and Biotechnology NSTDA Thailand

ISBN: (纸本)9781467321860

关键词： Satellite imagery

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Estimation of Low Resolution Visible Spectra from RGB imagery ii - Simulation Result

Estimation of Low Resolution Visible Spectra from RGB Imager...

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

作者： Schau, H. C. Meridian Syst LLC Titusville FL 32780 USA

ISBN: (纸本)9780819486226

While able to measure the red, green, and blue channels, color imagers are not true spectral imagers capable of spectral measurements. In a previous paper [1], it was demonstrated that an estimate of a low resolution visible spectra of a naturally illuminated outdoor scene can be estimated from RGB values measured by a conventional color imager. In this paper we present a refined algorithm and document results in a study to estimate visible source spectra from solar illumination scenes using reflectance spectra generated from the USGS data base.

关键词： hyperspectral Imaging multispectral Imaging Ultraspectral Imaging Imaging Spectroscopy Color Image RGB Imager

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Enhancement of Flow-Like Structures in hyperspectral imagery using Tensor Nonlinear Anisotropic Diffusion

Enhancement of Flow-Like Structures in Hyperspectral Imagery...

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

作者： Marin-McGee, Maider Velez-Reyes, Miguel Univ Puerto Rico Lab Appl Remote Sensing & Image Proc Mayaguez PR USA

ISBN: (纸本)9780819486226

Analyzing flow-like patterns in images for image understanding is an active research area but there have been much less attention paid to the process of enhancement of those structures. The completion of interrupted lines or the enhancement of flow-like structures is known as Coherence-Enhancement (CE). In this work, we are studying nonlinear anisotropic diffusion filtering for coherence enhancement. Anisotropic diffusion is commonly used for edge enhancement by inhibiting diffusion in the direction of highest spatial fluctuation. For CE, diffusion is promoted along the direction of lowest spatial fluctuation in a neighborhood thereby taking into account how strongly the local gradient of the structures in the image is biased towards that direction. Results of CE applied multispectral and hyperspectral images are presented.

关键词： hyperspectral Image Processing Nonlinear Diffusion Coherence-Enhancement Image Processing

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Selecting training and test images for optimized anomaly detection algorithms in hyperspectral imagery through robust parameter design

Selecting training and test images for optimized anomaly det...

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

作者： Mindrup, Frank M. Friend, Mark A. Bauer, Kenneth W. USAF Inst Technol Wright Patterson AFB OH USA

ISBN: (纸本)9780819486226

There are numerous anomaly detection algorithms proposed for hyperspectral imagery. Robust parameter design (RPD) techniques have been applied to some of these algorithms in an attempt to choose robust settings capable of operating consistently across a large variety of image scenes. Typically, training and test sets of hyperspectral images are chosen randomly. Previous research developed a framework for optimizing anomaly detection in HSI by considering specific image characteristics as noise variables within the context of RPD;these characteristics include the Fisher's score, ratio of target pixels and number of clusters. This paper describes a method for selecting hyperspectral image training and test subsets yielding consistent RPD results based on these noise features. These subsets are not necessarily orthogonal, but still provide improvements over random training and test subset assignments by maximizing the volume and average distance between image noise characteristics. Several different mathematical models representing the value of a training and test set based on such measures as the D-optimal score and various distance norms are tested in a simulation experiment.

关键词： hyperspectral imagery robust parameter design anomaly detection training/test set selection

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Ship Detection in MODIS imagery

Ship Detection in MODIS Imagery

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

作者： Dorado-Munoz, Leidy P. Velez-Reyes, Miguel Univ Puerto Rico Lab Appl Remote Sensing & Image Proc Mayaguez PR 00680 USA

ISBN: (纸本)9780819486226

Understanding the capabilities of satellite sensors with spatial and spectral characteristics similar to those of MODIS for Maritime Domain Awareness (MDA) is of importance because of the upcoming NPOES with 100 minutes revisit time carrying the MODIS-like ViiRS multispectral imaging sensor. This paper presents an experimental study of ship detection using MODIS imagery. We study the use of ship signatures such as contaminant plumes in clouds and the spectral contrast between the ship and the sea background for detection. Results show the potential and challenges for such approach in MDA.

关键词： Ship Detection MODIS imagery Target Detection Ship Plumes Maritime Domain Awareness NPOESS

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