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

共 387 条记录，以下是121-130 订阅

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An analysis of the probability distribution of spectral angle and Euclidean distance in hyperspectral remote sensing using microspectroscopy

An analysis of the probability distribution of spectral angl...

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

作者： Resmini, Ronald G. Deloye, Christopher J. Allen, David W. Mitre Corp 7594 Colshire DrMS T630 Mclean VA 22102 USA Natl Inst Stand & Technol Gaithersburg MD 20899 USA

ISBN: (纸本)9780819495341

Determining the probability distribution of hyperspectral imagery (HSI) data and of the results of algorithms applied to those data, is critical to understanding algorithm performance and for establishing performance metrics such as probability of detection, false alarm rate, and minimum detectable and identifiable quantities. The results of analyses of visible/near-infrared (VNIR;400 nm to 900 nm) HSI microscopy data of small fragments (similar to 1.25 cm in size) of minerals are presented. HST microscopy, also known as microspectroscopy, is the acquisition of HST data cubes of fields of view ranging from centimeters to millimeters in size. It is imaging spectrometry but at a small spatial scale. With HST microspectroscopy, several thousand spectral signatures may be easily acquired of individual target materials samples of which may be quite small With such data, probability distributions may be very precisely determined. For faceted/irregularly shaped samples and mixtures (checkerboard, intimate, or microscopic), HSI microscopy data readily facilitate a detailed assessment of the contribution of the materials, their morphology, spectral mixing interactions, radiative transfer processes, view/illumination geometry contributions, etc., to the observed probability distribution(s) of the HST data and of algorithm output. Here, spectral angle, the individual components of spectral angle (e.g., the inner product or numerator of the spectral angle equation), Euclidean distance, and L1 norm values are calculated. Regions of interest (ROT) on the fragments are easily defined that contain thousands of spectra far from the fragments' edges though translucency sometimes remains a factor impacting spectral signatures. The aforementioned metrics are derived for the spectra in an ROT of individual mineral fragments;across ROIs of different minerals;and with an ROT of an inert background. The resulting probability distributions of the various populations of the metrics are decid

关键词： hyperspectral remote sensing hyperspectral imagery HSI microscopy HSI microscopy spectral angle microspectroscopy Euclidean distance probability distribution L1 norm

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Fuzzy Decision Tree Model Adaptation to Multi- and hyperspectral imagery Supervised Classification

Fuzzy Decision Tree Model Adaptation to Multi- and Hyperspec...

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9th International conference on Digital Technologies (DT)

作者： Stankevich, Sergey Levashenko, Vitaly Zaitseva, Elena Natl Acad Sci Ukraine Sci Ctr Aerosp Res Earth Kiev Ukraine Univ Zilina Dept Informat Zilina Slovakia

ISBN: (纸本)9781479909247;9781479909230

Now the land cover classification system is very important for various remote sensing applications and many sectors of economy. Therefore, development of algorithms for multi- and hyperspectral imagery classification is an urgent task. In this paper we present a new efficient algorithm for multi- and hyperspectral imagery classification based on fuzzy decision tree approach. We use the multispectral imagery spectral bands as fuzzy data source attributes and cumulative mutual information between them and the resulting fuzzy classification as decision tree inducing criterion. Proposed algorithm provides classification accuracy than traditional ones and significant data dimensionality reduction by means of informative spectral bands selection.

关键词： remote sensing imagery classification fuzzy decision trees spectral band selection

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Undercomplete Learned Dictionaries for Land Cover Classification in multispectral imagery of Arctic Landscapes using CoSA: Clustering of Sparse Approximations

Undercomplete Learned Dictionaries for Land Cover Classifica...

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

作者： Moody, Daniela I. Brumby, Steven P. Rowland, Joel C. Gangodagamage, Chandana Los Alamos Natl Lab POB 1663 Los Alamos NM 87545 USA

ISBN: (纸本)9780819495341

Techniques for automated feature extraction, including neuroscience-inspired machine vision, are of great interest for landscape characterization and change detection in support of global climate change science and modeling. We present results from an ongoing effort to extend machine vision methodologies to the environmental sciences, using state-of-the-art adaptive signal processing, combined with compressive sensing and machine learning techniques. We use a Hebbian learning rule to build undercomplete spectral-textural dictionaries that are adapted to the data. We learn our dictionaries from millions of overlapping multispectral image patches and then use a pursuit search to generate classification features. Land cover labels are automatically generated using our CoSA algorithm: unsupervised Clustering of Sparse Approximations. We demonstrate our method using multispectral Worldview-2 data from three Arctic study areas: Barrow, Alaska;the Selawik River, Alaska;and a watershed near the Mackenzie River delta in northwest Canada. Our goal is to develop a robust classification methodology that will allow for the automated discretization of the landscape into distinct units based on attributes such as vegetation, surface hydrological properties, and geomorphic characteristics. To interpret and assign land cover categories to the clusters we both evaluate the spectral properties of the clusters and compare the clusters to both field- and remote sensing-derived classifications of landscape attributes. Our work suggests that neuroscience-based models are a promising approach to practical pattern recognition problems in remote sensing.

关键词： land cover classification multispectral learned dictionaries undercomplete dictionaries clustering of sparse approximations Hebbian learning spectral-textural features unsupervised classification

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Automated geo/ortho registered aerial imagery product generation using the Mapping System Interface Card (MSIC)

Automated geo/ortho registered aerial imagery product genera...

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

作者： Bratcher, Tim Kroutil, Robert Lanouette, Andre Lewis, Paul E. Miller, David Shen, Sylvia Thomas, Mark Ionetrics Inc. (United States) Kalman Corp. (United States) LR Tec Inc. (Canada) National Geospatial-Intelligence Agency (United States) The Aerospace Corp. (United States) U.S. Environmental Protection Agency (United States)

ISBN: (纸本)9780819495341

The development concept paper for the MSIC system was first introduced in August 2012 by these authors'. This paper describes the final assembly, testing, and commercial availability of the Mapping System Interface Card (MSIC). The 2.3kg MSIC is a self-contained, compact variable configuration, low cost real-time precision metadata annotator with embedded INS/GPS designed specifically for use in small aircraft. The MSIC was specifically designed to convert commercial-off-the-shelf (COTS) digital cameras and imaging/non-imaging spectrometers with Camera Link standard data streams into mapping systems for airborne emergency response and scientific remote sensing applications. COTS digital cameras and imaging/non-imaging spectrometers covering the ultraviolet through long-wave infrared wavelengths are important tools now readily available and affordable for use by emergency responders and scientists. The MSIC will significantly enhance the capability of emergency responders and scientists by providing a direct transformation of these important COTS sensor tools into low-cost real-time aerial mapping systems.

关键词： automated geo-registration automated ortho-rectification airborne mapping system data annotation configurable low cost aerial mapping system PCIe GigE IRIG-B INS GPS

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MEAN-SHIFT CLUSTERING FOR INTERACTivE multispectral IMAGE ANALYSIS

MEAN-SHIFT CLUSTERING FOR INTERACTIVE MULTISPECTRAL IMAGE AN...

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IEEE International conference on Image Processing

作者： Johannes Jordan Elli Angelopoulou Pattern Recognition Lab University of Erlangen-Nuremberg Germany

ISBN: (纸本)9781479923427

Mean shift clustering and its recent variants are a viable and popular image segmentation tool. In this paper we investigate mean shift segmentation on multispectral and hyperspectral images and propose three new algorithms. First, we improve segmentation performance by running mean shift on the spectral gradient. At the same time, we adapt a popular superpixel segmentation method to the multispectral domain using modified similarity measures from spectral mapping. Based on superpixels, we design two mean shift variants that both obtain competitive segmentation results in significantly reduced running time. For one variant, the speedup in our benchmark is over 100 times. This enables mean shift clustering in an interactive setting.

关键词： multispectral imaging hyperspectral imaging Image Segmentation Clustering algorithms Distance measurement distance measurement image segmentation multispectral imagery Clustering algorithms image analysis hyperspectral imagery multispectral Shift Cluster Analysis segmentation Segmentation

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Fuzzy decision tree model adaptation to multi- and hyperspectral imagery supervised classification

Fuzzy decision tree model adaptation to multi- and hyperspec...

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International conference on Digital Technologies (DT)

作者： Sergey Stankevich Vitaly Levashenko Elena Zaitseva Scientific Centre for Aerospace Research of the Earth National Academy of Sciences of Ukraine Kiev Ukraine Department of Informatics University of Žilina Zilina Slovakia

ISBN: (纸本)9781479909230

关键词： hyperspectral imaging Classification algorithms Decision trees Satellites Image classification

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Identification of bamboo patches in the lower Gangetic plains using very high resolution WorldView 2 imagery

Identification of bamboo patches in the lower Gangetic plain...

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conference on Earth Resources and Environmental Remote Sensing/GIS Applications iv

作者： Ghosh, Aniruddha Joshi, P. K. TERI Univ Dept Nat Resources New Delhi 110070 India

ISBN: (纸本)9780819497635

Realizing the potential usage, the country has identified National Bamboo mission for addressing the issues relating to the development of bamboo in the country. Therefore knowledge of spatial distribution of bamboo patches becomes necessary for the evaluation and monitoring of this resource. Medium resolution satellite imagery is ineffective for accurate classification when bamboo occurs in mosaic of small and mixed patches. The present study attempts to address this problem using Very High Resolution (VHR) multispectral (MS) WorldView 2 (WV 2) imagery in South 24 Parganas, West Bengal. West Bengal, a part of Bengal Bay ecological region is one of the areas where bamboo grows naturally. The classification was carried out using additional features namely second order texture components of the first 3 principal components (PCs) of pan-sharpened 8 MS bands. Supervised kernel based (Support Vector Machine, SVM) and ensemble based (Random Forest, RF) machine learning algorithms were applied on the dataset. Moreover, RF based variable importance was analyzed to find the most informative input predictors for classification of bamboo. Altogether seven land use land cover classes were mapped which include agricultural land, built-up, bamboo patches, two canopy classes, fallow land and water body. Variable importance analysis indicated that mean texture measure of PC1 and PC3, and spectral information of MS band 8 (NIR 2) were the most important predictor layers for bamboo mapping. Unlike, RF (accuracy 72%) higher overall accuracy of 80% was achieved using SVM classifier for bamboo mapping. Intermixing of bamboo was seen with other canopies.

关键词： Bamboo mapping Random Forest Support Vector Machine Texture variable importance WorldView 2

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Unsupervised unmixing analysis based on multiscale representation

Unsupervised unmixing analysis based on multiscale represent...

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

作者： Torres-Madronero, Maria C. Velez-Reyes, Miguel Univ Puerto Rico Lab Appl Remote Sensing & Image Proc Mayaguez PR 00681 USA

ISBN: (纸本)9780819490681

Automated unmixing consists of finding the number of endmembers, their spectral signatures and their abundances from a hyperspectral image. Most unmixing techniques are pixel-to-pixel procedures that do not take advantage of spatial information provided by hyperspectral sensor. This paper explores a new approach for unmixing analysis of hyperspectral imagery based on a multiscale representation for the joint estimation of the number of endmember and their spectral signatures. Experimental results using an AVIRIS image is presented.

关键词： hyperspectral imagery unsupervised unmixing multiscale representation

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A method for enhancing spectral resolution of multispectral satellite imagery

A method for enhancing spectral resolution of multispectral ...

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conference on multispectral, hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications iv

作者： Guo, Tao Kujirai, Toshihiro Watanabe, Takashi Kitano, Yu Zhao, Yu Hitachi Ltd Cent Res Lab Kokubunji Tokyo 1858601 Japan

ISBN: (纸本)9780819492661

It's essential but challenging to retrieve spectral features as detailed as possible in current satellite imagery industry. In this research, based on the physical model of sensor response function, we present a method to recover the reflective spectrum at the front end of sensor in an iterative way and to greatly enhance the spectral details of satellite imagery. Our method is able to largely increase the cost-performance ratio of current satellite multispectral imagery and also reveals great potentials of satellite imagery in various disciplines.

关键词： multispectral spectral resolution sensor response function satellite imagery

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Developing high resolution clutter for wireless network propagation using Worldview-2 imagery

Developing high resolution clutter for wireless network prop...

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

作者： Gwata, Batsi ComputaMaps Cape Town South Africa

ISBN: (纸本)9780819490681

Technological advancement has lead to an increased demand for up-to-date, high resolution solutions for Wireless Network Propagation. This research paper serves to highlight how 50cm resolution clutter classes can be accurately extracted from WorldView-2 imagery using semi-automated processes. Once the clutter classes have been extracted, a 3D model of the area is created for visualization purposes. The study area for this research was Rustenburg, South Africa.

关键词： Clutter Wireless Network Propagation Worldview-2 imagery

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