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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421-430 订阅

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Research issues in image registration for remote sensing

Research issues in image registration for remote sensing

引用

IEEE conference on Computer Vision and pattern recognition

作者： Eastman, Roger D. Le Moigne, Jacqueline Netanyahu, Nathan S. Loyola Coll Baltimore MD 21210 USA NASA Goddard Space Flight Ctr Greenbelt MD USA Bar Ilan Univ Ramat Gan Israel Univ Maryland College Pk MD USA

ISBN: (纸本)9781424411795

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

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High resolution remote sensing image classification with multiple classifiers based on mixed combining rule - art. no. 67881J

High resolution remote sensing image classification with mul...

引用

conference on pattern recognition and Computer Vision

作者： Chen, Zhong Liu, Jianguo Wang, Guoyou Li, Qiaoliang Huazhong Univ Sci & Technol Inst Pattern Recognit & Artificial Intelligence Wuhan 430074 Peoples R China

ISBN: (纸本)9780819469526

The development of the remote sensing technology makes us obtain very abundant information of nature, especially with the appearance of high resolution remote sensing image it extends the visual field of the nature. High-resolution satellite images such as Quickbird and IKONOS have been applied into many fields. But the challenge that faces us is how to make use of the data effectively and obtain more useful information through some processing. Because in the target recognition, the mutual-complementarity among the different results obtained by the different classifier making using of the same features usually is very strong and high resolution remote sensing data have a lot of characteristics such as spectral, texture and context and so on compared to the other lower resolution remote sensing data, the Multiple Classifiers making use of multi-characteristic was proposed to improve the high resolution remote sensing image classification accuracy in this paper. The experiments show that the approach can obtain higher classification accuracy and better classification result than single classifier.

关键词： multiple classifiers multi-characteristic high resolution remote sensing image

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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

5th International conference on Wavelet Analysis and pattern recognition

作者： Zhi, Zhang Xia, De-Shen Nanjing Univ Sci & Technol Dept Comp Inst Technol Nanjing 210094 Peoples R China

ISBN: (纸本)9781424410651

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 inlerpolation 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 mulli-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 mulli-degree interpolation and denoising, method is proved to be more, effective comparing with other common methods. The multi-degree filling 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： filtering multi-degree shift invariance remote sensing image

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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

5th International conference on Wavelet Analysis and pattern recognition

作者： Wang, Man Zhang, Jie-Lin Cao, Dai-Yong China Univ Min & Technol Sch Resource & Safety Engn Beijing 100083 Peoples R China Beijing Res Inst Uranium Geol Natl Lab Remote Sensing Beijing 100029 Peoples R China

ISBN: (纸本)9781424410651

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： curvelet transform IHS transform image fusion landsat enhanced thematic mapper plus(ETM plus ) image

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Application of Ant Colony Optimization (ACO) algorithm to remote sensing image classification - art. no. 67881A

Application of Ant Colony Optimization (ACO) algorithm to re...

引用

conference on pattern recognition and Computer Vision

作者： Dai, Qin Liu, Jianbo Chinese Acad Sci Key Lab China Remote Sensing Satellite Ground Stn Beijing 100086 Peoples R China

ISBN: (纸本)9780819469526

Ant Colony Optimization (ACO) algorithm takes inspiration from the coordinated behavior of ant swarms, which has been applied in many study fields as a novel evolutionary technology to solve optimization problems. But it has rarely been used to process remote sensing data. Using the ACO algorithm to remote sensing image classification does not assume an underlying statistical distribution for the pixel data, the contextual information can be taken into account, and it has strong robustness. In this paper, taking Landsat TM data as an example, the process of ACO method in remote sensing data classification is introduced in detail, and has achieved a good result. The study results suggest that ACO become a new effective method for remote sensing data processing.

关键词： Ant Colony Optimization (ACO) classification rules mining remote sensing image classification

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Gaussian process classification using image deformation

Gaussian process classification using image deformation

引用

32nd IEEE International conference on Acoustics, Speech and Signal processing

作者： Williams, David P. Signal Innovat Grp Res Triangle Pk NC USA

ISBN: (纸本)1424407281

An image deformation algorithm is integrated with a Gaussian process classifier for application to remote-sensing tasks in which data is in the form of imagery. To combine these disparate techniques, we introduce a novel kernel covariance function for the Gaussian process that allows us to incorporate the result of the image deformation algorithm into a rigorous Bayesian classification framework. The resulting classifier is completely non-parametric in the sense that no parameters or hyperparameters must be learned. The promise of the proposed algorithm is demonstrated on a data set of real, measured land mine data.

关键词： classification image deformation Gaussian processes remote sensing automatic target recognition

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Novel framework for producing multi-scale and multi-viewpoint images based on remote sensing stereopair - art. no. 67881G

Novel framework for producing multi-scale and multi-viewpoin...

引用

conference on pattern recognition and Computer Vision

作者： Tan, Ying Cao, Zhiguo Li, Yukun Huazhong Univ Sci & Technol Inst Pattern Recognit & Artificial Intelligence State Key Lab Multispectral Informat Proc Technol Wuhan 430074 Peoples R China

ISBN: (纸本)9780819469526

Automatic target recognition(ATR) is the key of the image guidance technology, yet it is difficult to recognize the target by merely depending on the real-time image acquired by flying vehicle cameras, moreover, the task of recognizing the target from the real-time images by the vehicle-carrying image processing system is a hard work itself The main trend of the ATR nowadays is to make utilization of the images produced by high-resolution remote sensing satellite to retrieve the front elevation of the interested region before hand. These front elevations are loaded upon the flying vehicles and are matched with the real-time images acquired by vehicle-carrying cameras to recognize the interested target. Obviously, the key step of this method is to recover the 3D information from 21) images. This paper proposed a framework to produce multi-scale and multi-viewpoint projection images based on remote sensing satellite stereopair by means of photogrammetry and computer vision. First we proposed a algorithm for reconstructing the 3D structure of the target by digital photogrammetric techniques and establishing the 3D model of the target using the OpenGL visualization toolkit. Then the conversion relationship between the world coordinate system and the simulation space coordinate system is provided to produce the front elevation in the simulation space.

关键词： 3D reconstruction multi-scale multi-viewpoint front elevation gauss plane gauss coordinates system simulation space

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Three-dimensional passive sensing photon counting for object classification

Three-dimensional passive sensing photon counting for object...

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conference on Automatic Target recognition XVii

作者： Yeom, Seokwon Javidi, Bahram Watson, Edward Daegu Univ Sch Comp & Commun Engn Jillyang 712714 Gyeongbok South Korea Univ Connecticut Dept Elect & Comp Engn U 2157 Storrs CT 06269 USA US Air Force Res Lab Wright Patterson AFB OH 45433 USA

ISBN: (纸本)9780819466884

In this keynote address, we address three-dimensional (3D) distortion-tolerant object recognition using photon-counting integral imaging (ii). A photon-counting linear discriminant analysis (LDA) is discussed for classification of photon-limited images. We develop a compact distortion-tolerant recognition system based on the multiple-perspective imaging of ii. Experimental and simulation results have shown that a low level of photons is sufficient to classify out-of-plane rotated objects.

关键词： automatic target recognition pattern recognition and feature extraction rotation-invariant pattern recognition three-dimensional image processing photon counting integral imaging passive sensing

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A knowledge based classification for urban mapping using high resolution remote sensing data

A knowledge based classification for urban mapping using hig...

引用

Australian pattern recognition Society (APRS)

作者： Yi, Xiao Jia, Xiuping Tien, David School of Information Technology Charles Sturt University Panorama Avenue Bathurst NSW 2795 Australia University College University of New South Wales Australian Defence Force Academy Campbell ACT 2600 Australia

ISBN: (纸本)0769530672

This paper addresses the weakness of pixel based or cell-based classification algorithms for urban mapping. They cannot provide a handy object level classification results as often preferred in urban planning and assessment applications. A knowledge based approach is proposed which is integrated with the spectral classification methods and image processing tools. Experiments using a high resolution image were conducted. The results show the proposed method can improve the performances obtained by using the conventional maximum likelihood or ECHO algorithms. © 2007 IEEE.

关键词： Maximum likelihood

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Clustering polarimetric SAR image under deorientation theory

Clustering polarimetric SAR image under deorientation theory

引用

32nd IEEE International conference on Acoustics, Speech and Signal processing

作者： Kang, Xin Han, Chongzhao Xu, Feng Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Peoples R China Fudan Univ Minist Educ Res Key Lab Wave Scattering & Remote Sensing Informat Shanghai 200433 Peoples R China

ISBN: (纸本)9781424407286

In natural complex terrain surfaces, scattering targets with random orientations produce random fluctuating echoes which lead to confused classifications by directly using target decomposition on polarimetric SAR (PolSAR) image. In order to reduce the influence, the target vector is transformed into the state with minimization of cross-polarization. Then a set of new parameters u/v/w are used to characterize scattering mechanisms under the deorientation theory, and the fuzzy membership is adopted instead of "hard" division of parameter plan. Characterizing the sample coherency matrices as complex Wishart distribution, the PolSAR image is clustered based on Bayes Maximum Likelihood (ML) criteria. Experiment is carried out on an L-band NASA/JPL SIR-C PolSAR image over Danshui town, Guangdong, China. Comparison results with the popular used methods show that the proposed method provides a significant improvement in classification and the associated scattering mechanism of class is more accurate and beneficial for automatic terrain recognition.

关键词： deorientation pattern classification fuzzy clustering radar polarimetry SAR

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