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检索条件"任意字段=Conference on Image Processing and Pattern Recognition in Remote Sensing II"

共 707 条记录，以下是431-440 订阅

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Research issues in image registration for remote sensing

Research issues in image registration for remote sensing

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conference on Computer Vision and pattern recognition (CVPR)

作者： Roger D. Eastman Jacqueline Le Moigne Nathan S. Netanyahu Loyola College Baltimore MD USA NASA Goddard Space Flight Center Greenbelt MD USA University of Maryland College Park MD USA Bar-llan University Israel

image registration is an important element in data processing for remote sensing with many applications and a wide range of solutions. Despite considerable investigation the field has not settled on a definitive solution for most applications and a number of questions remain open. This article looks at selected research issues by surveying the experience of operational satellite teams, application-specific requirements for Earth science, and our experiments in the evaluation of image registration algorithms with emphasis on the comparison of algorithms for subpixel accuracy. We conclude that remote sensing applications put particular demands on image registration algorithms to take into account domain-specific knowledge of geometric transformations and image content.

关键词： image registration remote sensing Data processing Satellite broadcasting Production Educational institutions NASA image sensors Biomedical imaging Geoscience

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Fusion of multispectral and panchromatic satellite images based on ihs and curvelet transformations

Fusion of multispectral and panchromatic satellite images ba...

引用

International conference on Wavelet Analysis and pattern recognition (ICWAPR)

作者： Man Wang Jie-Lin Zhang Dai-Yong Cao School of Resource and Safety engineering China University of Mining and Technology Beijing China Beijing Research Institute of Uranium Geology National Laboratory of Remote Sensing Beijing China

image fusion is an important tool for remote sensing data processing technology, as many Earth observation satellites provide both high-resolution panchromatic and low-resolution multispectral images. This paper presents a new image fusion method that combines IHS transform and curvelet transform. Experiments carried out on a enhanced thematic mapper plus image show that the proposed method quantitatively outperforms state-of-the art image fusion methods, in terms of geometric, radiometric, and spectral fidelity.

关键词： image fusion remote sensing Wavelet analysis Wavelet transforms image analysis pattern analysis pattern recognition Satellite broadcasting Principal component analysis

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OSIRIS: a European project using a High Altitude Platform for forest fire monitoring

OSIRIS: a European project using a High Altitude Platform fo...

引用

2nd International conference Safety and Security Engineering

作者： Lewyckyj, N. Biesemans, J. Everaerts, J. Flemish Inst Technol Res VITO TAP Remote Sensing & Earth Observ Proc Unit Mol Belgium

ISBN: (纸本)9781845640682

Within the framework of the European FP6 IST-5 project OSIRIS, a High Altitude Platform (HAP) will be used for the first time for surveying forest fires in the South of France. The HAP used will be the Mercator-1 Unmanned Aerial Vehicle (UAV) acquired by the Flemish Institute for Technological Research (VITO) in Belgium. Mercator-1, a solar aircraft able to remain for days at stratospheric altitudes (14 to 20 km), will be used as a platform for a high resolution camera (GSD of a few meters). The platform will be controlled directly by an operator at a mobile Ground Control Station (GCS) or execute pre-defined flight planning (way points), which can be updated during flight. This will allow the end-users to optimize the image acquisition pattern as a function of the fire evolution. Because of its low airspeed, the platform is able to hover above a small region. Unprocessed data (level 0) will be down linked in real-time to the GCS for further processing (up to level 3 if required). Decision makers and/or the commanding officer of the fire brigades will thus have rapid access to up-to-date information at the intervention place. Furthermore, by means of a satellite uplink, the imagery will also be forwarded to a Central Data processing Centre which is plugged into the OSIRIS Sensor Web. During the OSIRIS demonstration activities the benefits of the Sensor Web concept with respect to faster and/or better decision making will be evaluated. The unmanned character of the platform does not put any pilots at risk and its long endurance allows the HAP to be used for the different phases of a disaster (from preparedness to recovery). Furthermore, the system will provide high resolution combined with frequent image update. A live demonstration is planned for spring 2009.

关键词： disaster management forest fire remote sensing real-time high resolution HAP UAV PEGASUS OSIRIS

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remote sensing image matching based on corner structures

Remote sensing image matching based on corner structures

引用

International conference on image and Graphics (ICIG)

作者： Hongya Tuo Zhongliang Jing Yuncai Liu Institute of Aerospace Sci. & Tech. Shanghai Jiaotong University Shanghai China Inst. of Image Processing & Pattern Recognition Shanghai Jiaotong University Shanghai China

In this paper, we propose a matching method for remote sensing images based on corner structures. Firstly corner angle vector is defined to analysis corner structure, and the process of how to obtain it is discussed in detail. Then some measures are given to eliminate the false corners. Finally a relaxation matching scheme is presented. The experiments show the effectiveness and feasibility of our matching method.

关键词： remote sensing image matching Arm Computer vision Detectors Bismuth pattern recognition image edge detection Graphics image processing

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A filting method of multi-degree shift invariant wavelet based on satellite image

A filting method of multi-degree shift invariant wavelet bas...

引用

International conference on Wavelet Analysis and pattern recognition (ICWAPR)

作者： Zhang Zhi De-Shen Xia Department of Computer Institute of Technology Nanjing University of Science and Technology of PLA Nanjing China

To avoid the degraded artifacts during satellite image acquisition, the subpixel technology is commonly used, which is interlacingly sampled with two images offsetting half a pixel of the same scene. However, the common interpolation is not highly precise. The reason is due to the fact that they are linear and local. In order to get a high degree of satellite image resolution, the paper proposes a multi-degree shift invariant complex wavelet filtering method. Based on the complex wavelet filtering method, the higher resolution remote sensing images can be attained. Accordingly, the algorithm of adaptive shrinkage complex wavelet denoising based inter-scale is combined with the algorithm mentioned above. The multi-degree interpolation and denoising method is proved to be more effective comparing with other common methods. The multi-degree filting method is suitable for processing the complicated remote sensing images, and can reconstruct the high-quality image perfectly from the multi-directional subimages.

关键词： Satellites Interpolation image resolution Filtering remote sensing Noise reduction Degradation Pixel Layout image reconstruction

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A three-stage approach based on the self-organizing map for satellite image classification

引用

17th International conference on Artificial Neural Networks (ICANN 2007)

作者： Goncalves, Mdrcio L. Netto, Marcio L. A. Costa, Jose A. F. Pontificia Univ Catolica Minas Gerais Dept Comp Sci Pocos De Caldas MG Brazil Univ Estadual Campinas Sch Elect Engn Campinas SP Brazil Univ Fed Rio Grande do Norte Dept Elect Engn BR-59072970 Natal RN Brazil

ISBN: (纸本)9783540746935

This work presents a methodology for the land-cover classification of satellite images based on clustering of the Kohonen's self-organizing map (SOM). The classification task is carried out using a three-stage approach. At the first stage, the SOM is used to quantize and to represent the original patterns of the image in a space of smaller dimension. At the second stage of the method, a filtering process is applied on the SOM prototypes, wherein prototypes associated to input patterns that incorporate more than one land cover class and prototypes that have null activity are excluded in the next stage or simply eliminated of the analysis. At the third and last stage, the SOM prototypes are segmented through a hierarchical clustering method which uses the neighborhood relation of the neurons and incorporates spatial information in its merging criterion. The experimental results show an application example of the proposed methodology on an IKONOS image.

关键词： pattern recognition self-organizing maps image processing remote sensing

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Model based compression of the calibration matrix for hyperspectral imaging systems

Model based compression of the calibration matrix for hypers...

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conference on Mathematics of Data/image pattern Recongnition, Compression, Coding, and Encryption X, with Applications

作者： Scholl, James F. Hege, E. Keith O'Connell, Daniel Dereniak, Eustace L. Univ Arizona Coll Opt Sci 1630 E Univ Blvd Tucson AZ 85721 USA MKS Imaging Technol LLC Tucson AZ 85718 USA Univ Arizona Dept Astron Steward Observ Tucson AZ 85721 USA Hun Photon Wailuku HI 96793 USA

ISBN: (纸本)9780819468482

In hyperspectral imaging systems with a continuous-to-discrete (CD) model, the goal is to solve the matrix equation g = H theta + n for theta. Here g is a data vector obtained on pixels on a focal plane array (FPA), and n is the additive pixel noise vector. The hyperspectral object cube f(x, y, lambda) to be recovered is represented by theta, which is the vectorized set of expansion coefficients of f with respect to a family of functions. The imaging operator is the system matrix H of which its columns represent the projection of each expansion function onto the FPA. Hence an estimate of the object cube f(x, y, lambda) is reconstructed from these recovered expansion function projection coefficients. Furthermore H is equivalently a calibration matrix, and amenable to an analytic description. Since the number of expansion functions is large, and the number of pixels on an FPA is large, H becomes huge and very unwieldy to store. We describe a means by which we can reduce the effective size of H by taking advantage of the analytic model of the imaging system and converting H into a series of look-up tables. By this method we have been able to drastically reduce the storage requirements for H from terabytes to sub-megabyte sizes. We provide an example of this technique in isoplanatic and polychromatic calibration of a flash hyperspectral imaging system. These sets of lookup tables are expansion function independent and also independent of object cube sampling.

关键词： diffraction theory hyperspectral signal processing remote sensing

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MULTISPECTRAL remote sensing image CLASSIFICATION WITH MULTIPLE FEATURES

MULTISPECTRAL REMOTE SENSING IMAGE CLASSIFICATION WITH MULTI...

引用

2007 International conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

作者： QIAN YIN PING GUO Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 Chin Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 Chin

In this paper, we propose to combine the spectral and texture features to compose the multi-feature vectors for the classification of multispectral remote sensing *** usually is difficult to obtain the higher classification accuracy if only considers one kind feature, especially for the case of different geographical objects have the same spectrum or texture specialty for a multispectral remote sensing *** spectral feature and the texture feature are composed together to form a new feature vector, which can represent the most effective features of the given remote sensing *** this way we can overcome shortcomings of only using the single feature and raise the classification *** system classification performance with composed feature vector is investigated by *** analysis of results we can learn how to combine the multi-feature vector can obtain a higher classification rate, and experiments proved that the proposed method is feasible and useful in multispectral remote sensing image classification study.

关键词： Spectrum feature Texture feature Multispectral remote sensing image Feature combination Classification.

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A Hierarchical Finite-State Model for Texture Segmentation

A Hierarchical Finite-State Model for Texture Segmentation

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Giuseppe Scarpa Michal Haindl Josiane Zerubia DIET University Federico II Italy INRIA/I3S ARIANA Research Group France Pattern Recognition Dep. Academy of Sciences Prague Czech Republic

A novel model for unsupervised segmentation of texture images is presented. The image to be segmented is first discretized and then a hierarchical finite-state region-based model is automatically coupled with the data by means of a sequential optimization scheme, namely the texture fragmentation and reconstruction (TFR) algorithm. Both intra- and inter-texture interactions are modeled, by means of an underlying hierarchical finite-state model, and eventually the segmentation task is addressed in a completely unsupervised manner. The output is then a nested segmentation, so that the user may decide the scale at which the segmentation has to be provided. TFR is composed of two steps: the former focuses on the estimation of the states at the finest level of the hierarchy, and is associated with an image fragmentation, or over-segmentation; the latter deals with the reconstruction of the hierarchy representing the textural interaction at different scales.

关键词： image segmentation image reconstruction Clustering algorithms State estimation pattern recognition Biomedical imaging remote sensing Source coding System testing Benchmark testing

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International conference on Computing: Theory and Applications-Title

International Conference on Computing: Theory and Applicatio...

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International conference on Computing: Theory and Applications, ICCTA

The following topics are dealt with: parallel and distributed computing; mobile and wireless networks; VLSI design; high performance computing; pattern recognition and soft computing; artificial neural networks; fuzzy set theory; support vector machines; data mining; document processing; natural language processing; speech and signal processing; image processing; computer vision; content based image retrieval; remote sensing

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