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湖北农业科学 2011年第23期50卷 4831-4833页

作者：夏清童琳宾郑泽忠西南科技大学环境与资源学院四川绵阳621010 四川德阳矿产开发公司四川德阳618000 电子科技大学自动化工程学院成都611731

以Moffett Field的AVIRIS高光谱遥感影像为对象,应用基于信息量的自适应波段选择方法实现植被信息提取的最优波段组合,在此基础上采用监督分类为主、目视解译为辅的信息提取方法,在ENVI和ERDAS遥感图像处理软件中进行分析与处理,实现植... 详细信息

以Moffett Field的AVIRIS高光谱遥感影像为对象,应用基于信息量的自适应波段选择方法实现植被信息提取的最优波段组合,在此基础上采用监督分类为主、目视解译为辅的信息提取方法,在ENVI和ERDAS遥感图像处理软件中进行分析与处理,实现植被信息提取。结果表明,与多光谱遥感数据相比,高光谱遥感数据具有更强的植被信息识别能力,提取的植被信息更丰富。

关键词：高光谱遥感植被信息提取

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Classification of CASI-3 hyperspectral image by subspace method

Classification of CASI-3 hyperspectral image by subspace met...

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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Buho Hoshino Hasi Bagan Akihiro Nakazawa Masami Kaneko Masaki Kawai Tetuo Yabuki Department of Biosphere and Environmental Sciences Rakuno Gakuen University Ebetsu Hokkaido Japan Center for Global Environmental Research National Institute for Environmental Studies Tsukuba Ibaraki Japan Asia Air Survey Company Limited Kanagawa Japan Earth Remote Sensing Data Analysis Center Tokyo Japan

This study presents a supervised subspace learning classification method which can be applied directly to the original set of spectral bands of hyperspectral data for land cover classification purpose. The CLAss-Featuring Information Compression (CLAFIC) method is used to generate the appropriate feature subspace for each class on the training data set by Karhunen-Loeve transform (also known as the principal component analysis). Then, using the iterative learning technology of averaged learning subspace methods (ALSM) to rotate the subspaces slowly for optimizes the subspaces to get better classification accuracy. We carried out experiments with 68 spectral bands Compact Airborne Spectrographic imager-3 (CASI-3) data set. Experimental results show that Subspace method is a valid and effective alternative to other pattern recognition approaches for the mapping grass species and monitoring grass health using hyperspectral remote sensing data. Moreover, it is worth noting that the ALSMs are easily applied (i.e. they only request to set two parameters and can be directly applied to hyperspectral data) and they can entirely identify the training samples in a finite number of steps.

关键词： Training Hyperspectral imaging Accuracy Sensors Training data

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Spatial Knowledge Based Complicated Urban Area Classification from High-Resolution remote sensing image

Spatial Knowledge Based Complicated Urban Area Classificatio...

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2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services(第一届空间数据挖掘与地理知识服务国际学术会议 ICSDM 2011)

作者： Cheng Qiao Jiancheng Luo Zhanfeng Shen Zhiwen Zhu Wei Wu Institute of Remote Sensing Applications CAS No.20 Datun Rd. Chaoyang District P.O.Box9718 Beijing100101P.R China

Combining spectral and spatial information can improve land use classification of high-resolution data. However, the use of spatial information always focus on objects’ spatial pattern, whereas not pay enough attention to spatial relationship, which is more convenient and effective in remote sensing classification. This letter proposes a spectralspatial information method, which aims to exploit objects’ spatial relationships in high resolution imagery, and then integrate it with spectral information in remote sensing classification. We experiment on urban mapping based on spectral-spatial information using Quickbird imagery, and compare its result with supervised classification methods like maximum likelihood classification, and support vector machine (SVM) classification. The results show that the proposed method yield better performance than the others in both precision and rationality.

关键词： Spatial knowledge urban area classification remote sensing image processing

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Combining ensemble technique of support vector machines with the optimal kernel method for hyperspectral image classification

Combining ensemble technique of support vector machines with...

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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Bor-Chen Kuo I-Ling Chen Cheng-Hsuan Li Chih-Cheng Hung Graduate Institute of Educational Measurement and Statistics National Taichung University of Education Taichung Taiwan Department of Electrical and Control Engineering National Chiao Tung University Hsinchu Taiwan School of Computing and Software Engineering Southern Polytechnic State University GA USA

In remote sensing researches, the curse of dimensionality is one greatly difficult classification problem. Many studies have demonstrated that multiple classifier systems, such as the random subspace method (RSM), can alleviate small sample size and high dimensionality concern and obtain more outstanding and robust results than a single classifier on extensive pattern recognition issues. A dynamic subspace method (DSM) was proposed for constructing component classifiers with adaptive subspaces to adjust the shortcomings of RSM based on re substitution accuracy by applying each classifier. However, the performances of SVMs are based on choosing the proper kernel functions or proper parameters of a kernel function. The objective of this research is to develop a novel ensemble technique based on support vector machines (SVMs) via the optimal kernel method, and propose a novel subspace selection mechanism, named the kernel-based dynamic subspace method (KDSM), to improve DSM on automatically determining dimensionality and selecting component dimensions for diverse subspaces. Experimental results show a sound performance of classification on the famous hyperspectral images, Washington DC Mall.

关键词： Kernel Support vector machines Nickel Hyperspectral imaging Training Accuracy

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image registration and a metric to assess the transformation function

Image registration and a metric to assess the transformation...

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Applied imagery pattern recognition Workshop (AIPR)

作者： John Marshall Peter Doucette NGA Springfield VA USA

image registration has been a broadly applied topic across the photogrammetric/remote sensing and computer vision communities. It is a foundational step for many applications such geopositioning, data fusion, change detection, conflation, and object recognition and extraction. The efficacy of many automated geospatial processes can be limited or nullified by an inadequate registration process. The task of automated image registration presents two main challenges: 1) establishing image-to-image correspondence through feature matching, and 2) determining an appropriate transformation model for a given registration scenario. When imaging 3D environments, a goal of the transformation function is to accurately relate the 2D pixel spaces of candidate images with potential geometric distortions and surface discontinuities projected from a 3D object space. When sensor model metadata and 3D surface information is available (e.g. a digital surface model), a 3D-to-2D photogrammetric transformation will generally provide the most reliable registration solution. Moreover, photogrammetric solutions propagate error to provide a statistically rigorous estimation of registration accuracy. On the other hand, direct 2D-to-2D transformations such as affine, homographic, and polynomials are often used when sensor metadata and/or object space information is limited or unavailable. Owing to their convenience of use and implementation, direct 2D-to-2D registration methods abound in commercial software application. However, such registration solutions are generally more suspect in terms of accuracy and uncertainty estimation. Nonetheless, they do have practical utility, provided appropriate care is exercised in their application. The goal of this paper is to quantitatively demonstrate different scenarios and solutions that users should consider when applying 3D-to-2D photogrammetric versus direct 2D-to-2D image registration methods.

关键词： Three dimensional displays Solid modeling

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Memristor-Based pattern recognition for image processing: an Adaptive Coded Aperture Imaging and sensing Opportunity

Memristor-Based Pattern Recognition for Image Processing: an...

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Conference on Adaptive Coded Aperture Imaging, Non-Imaging, and Unconventional Imaging Sensor Systems II

作者： Yakopcic, Chris Taha, Tarek M. Subramanyam, Guru Shin, Eunsung Murray, P. Terrence Rogers, Stanley Univ Dayton Dayton OH 45469 USA

ISBN: (纸本)9780819483140

Adaptive coded aperture (diffraction) sensing, an emerging technology enabling real-time, wide-area IR/visible sensing and imaging, could benefit from new high performance biologically inspired image processing architectures. The memristor, a novel two terminal passive device can enable significantly powerful biologically inspired processing architectures. This device was first theorized by Dr. Leon Chua in 1971. In 2008, HP Labs successfully fabricated the first memristor devices. Due to its unique properties, the memristor can be used to implement neuromorphic functions as its dynamics closely model those of a synapse, and can thus be utilized in biologically inspired processing architectures. This paper uses existing device models to determine how device parameters can be tuned for the memristor to be used in neuromorphic circuit design. Specifically, the relation between the different models and the number of states the device can hold are examined.

关键词： Resistance Instrument modeling Oxygen image processing Oxides Titanium Titanium dioxide Coded aperture imaging Resistors Biosensing

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Simulation of complex target RCS with application to SAR image recognition

Simulation of complex target RCS with application to SAR ima...

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Asian and Pacific Conference on Synthetic Aperture Radar (APSAR)

作者： Cheng-Yen Chiang Kun-Shan Chen Center of Space and Remote Sensing Research National Central University of Taiwan Jhong Li Taiwan

SAR(synthetic aperture radar) image understanding and interpretation is essential for remote sensing of earth environment and target detection. In development of aided target recognition and identification system, SAR image database with rich information content plays important role is essential. This paper presents a RCS computation for simulation of orbital SAR image. After demodulation, the received SAR signal is given as [1-2] s 0 (τ, η) = A 0 w r (τ - (2R(η))/c, T p )w a (η) exp{- j (4πf c R(η))/c + jπα r (τ - (2R(η))/c) 2 + φ"} (1) where φ" is lumped sum of phase noise from atmosphere, satellite altitude error, terrain, etc. and R is distance from antenna to target being observed, A 0 is slant range backscatter coefficient of the target, φ the phase term, and w a (η) is antenna pattern and is a function of slow time. To take into account the radar backscattering characteristics in (5), we apply Radar Cross Section Analysis and Visualization System (RAVIS) [3] that utilizes the physical optics (PO), physical diffraction theory (PDT), and shooting and bouncing rays (SBR) to compute the RCS of complex radar targets [4-8]. Single scattering and diffraction from a target are first computed by PO and PDT, followed by SBR to account for multiple scattering and diffraction. The system outputs for a given 3D CAD model of the target of interest. The CAD model contains numerous grids or polygons, each associated with computed RCS as function of incident and aspect angles for a given set of radar parameters. The number of polygons is determined by target's geometry complexity and its electromagnetic size. To realize the imaging scenario, each polygon must be properly oriented and positioned based on proper coordinates system. SAR image is sensitive to target's geometry including orientation and aspects angles. For target recognition and identification, more complete database for feature extraction is preferable to achieve better performance and reduce false alarm rate.

关键词： Target recognition Solid modeling Computational modeling Radar cross section Atmospheric modeling Synthetic aperture radar Accuracy

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Heterogeneous face biometrics based on Guassian weights and invariant features synthesis

Heterogeneous face biometrics based on Guassian weights and ...

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International Conference on Computing Control and Industrial Engineering (CCIE)

作者： Mengyi Liu Wei Xie Xingwei Chen Yufeng Ma Yujing Guo Jing Meng Zhiyong Yuan Qianqing Qin School of Computer Wuhan University of China Wuhan China State Key Laboratory of Information Engineering in Surveying Mapping & Remote Sensing Wuhan University of China Wuhan China

Face images captured in different spectral bands are said to be heterogeneous. Although the heterogeneous face images from a same individual are significantly different in appearance, we can still achieve multi-modal patterns matching by image processing and transforming. In this paper, we propose a novel recognition algorithm based on face synthesis from NIR (near infrared) to VIS (visual light). For this first we use the illumination-invariant feature to construct face mapping function, then apply the correlation coefficient Gaussian kernel to determine the weights of synthesis components, and produce a synthesized VIS image corresponding to the query NIR image, thereby our problem is transformed to conventional homogeneous (VIS) face matching. Experimental results show that the proposed method effectively improves the recognition results.

关键词： Face Biometrics Lighting Face recognition Kernel Correlation Conferences

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Multiple-image compressed encryption and decryption by compressive holography

Multiple-image compressed encryption and decryption by compr...

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IEEE Visual Communications and image processing (VCIP)

作者： Hong Di Kangfeng Zheng Xinxin Niu Xin Zhang Information Security Center Beijing University of Posts and Telecommunications Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China

This paper presents a scheme of a multiple-image compressed encryption based on the compressive holography technique. Computer generate hologram (CGH) is implemented to record multiple images simultaneously into an encrypted hologram. Because its two-dimensional (2D) Fourier transform (FT) result is analogous a partial 3D Fourier transform sampling, the 2D FT result can be compressed by a nonuniform sampling accompanied with a quantization. The encryption and compression processes agrees with the requirement of the compressive sensing and composes the compressive holography. Therefore, the decryption is solved by a minimization. It remains the sparsity of the recovered natural images in the wavelet basis. Meanwhile, a total-variation regularization and a nonnegative constraint is employed to extract images with edge preserved and nonnegative gray scale, respectively. Experiments are conducted to demonstrate the feasibility of the multiple- image compressed encryption.

关键词： image coding Encryption Holography Quantization Optimized production technology image reconstruction PSNR

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Supervised classification of satellite imagery using Enhanced seeded region growing technique

Supervised classification of satellite imagery using Enhance...

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Nirma University International Conference on Engineering (NUiCONE)

作者： Astha Baxi Manoj Pandya M. H. Kalubarme M. B. Potdar

The image classifications techniques have been practiced by remote sensing experts following certain methods like unsupervised and supervised. Supervised classification requires precise human intervention to extract features. Enhanced Seeded region growing technique is an image segmentation method; where the image pixel is seeded by latitude and longitude recorded during ground truth data collection using GPS. The Enhanced seeded region growing technique generates clusters based upon 8 nearest neighbor pixel connections. pattern recognition standard software is trained for the spectral signatures of the corresponding pixels. Then the supervised classification algorithm can be used. The system can leverage the potential of Location based services (LBS) and Information Communication Technology (ICT) to dynamically pull the latitude and longitude from the server using web services and gateway protocols. This method requires less effort to extract features from the image. This scheme is applied on satellite imagery covering surendranagar district in Gujarat, India.

关键词： Classification algorithms Cotton Algorithm design and analysis image segmentation Satellites Training image color analysis

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