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

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Total inorganic nitrogen content distribution pattern in estuary region by means of hyperspectral remote sensing and water quality numeric simulation techniques

Total inorganic nitrogen content distribution pattern in est...

引用

International conference on Earth Observation Data processing and Analysis, ICEODPA

作者： Lin, Xiang Zhang, Dong Zhang, Ying Xu, Yong Li, Huan College of Ocean College of Traffic Hohai University Nanjing 210098 China School of Geographic Science Nanjing Normal University Nanjing 210046 China

Quantitative analysis of the temporal and spatial distribution characteristics of coastal nutrient substances enables to adequately estimate the state of coastal marine environment and describe environmental change processes conditioned by anthropogenic forces. remote sensing has the potential to provide synoptic information and has been somewhat successful in monitoring nutrient properties at rivers and estuaries. So taking total inorganic nitrogen (TIN) as typical nutrient monitoring index, Sheyang River estuary located in middle part of Jiangsu coastline, China was chosen for water quality simulation and variation trend analysis. Six correlation coefficient matrixes were calculated by using synchronous TIN concentration and its corresponding normalized water surface reflectance data from 15 field samples. Results showed that band combination of 804 and 630nm with the form of pseudo-sediment parameter could get the best correlation capacity and minimized reversion error. Based on this selected parameter, an inverse model was built for TIN quantitative reversion. R2 coefficients reached 0.97 and 0.9972 in calibration and validation period respectively. And then the spatial distribution pattern of TIN in Sheyanghe River estuary was obtained using the inverse model via Hyperion hyperspectral remote sensing image. A coupled wave-tide-surge model and material transport and diffusion model were adopted for TIN concentration cross validation of the reversion precision exactly at river outlet. Comparison results indicated that these two dataset made a good consistency for TIN diffusive characters in Sheyang River estuary with the R2 reached 0.6549. The magnitude of TIN concentration was also agreed fairly well. © 2008 SPIE.

关键词： Water quality

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Two-beam coupling correlation synthetic aperture radar image recognition with power-law scattering centers pre-enhancement

Two-beam coupling correlation synthetic aperture radar image...

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conference on Data Mining, Intrusion Detection, Information Assurance and Data Networks Security 2008

作者： Haji-saeed, Bahareh Khoury, Jed Woods, Charles L. Kierstead, John Solid State Sci Corp Hollis NH 03049 USA US Air Force Res Lab SNHC Hanscom AFB MA 01731 USA

ISBN: (纸本)9780819471642

Synthetic radar image recognition is an area of interest for military applications including automatic target recognition, air traffic control, and remote sensing. Here a dynamic range compression two-beam coupling joint transform correlator for detecting synthetic aperture radar (SAR) targets is utilized. The joint input image consists of a pre-power-law, enhanced scattering center of the input image and a linearly synthesized power-law enhanced scattering center template. Enhancing the scattering center of both the synthetic template and the input image furnishes the conditions for achieving dynamic range compression correlation in two-beam coupling. Dynamic range compression: (a) enhances the signal to noise ratio, (b) enhances the high frequencies relative to low frequencies, and (c) converts the noise to high frequency components. This improves the correlation peak intensity to the mean of the surrounding noise significantly. Dynamic range compression correlation has already been demonstrated to outperform many optimal correlation filters in detecting signals in severe noise environments. The performance is evaluated via established metrics, such as peak-to-correlation energy (PCE), Homer efficiency and correlation peak intensity. The results showed significant improvement as the power increased.

关键词： pattern recognition correlation synthetic aperture radar (SAR)

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Mapping of alpine grassland cover in western China from normalized Landsat TM image

Mapping of alpine grassland cover in western China from norm...

引用

conference on Optical and Digital image processing

作者： Zha, Yong Gao, Jay Nanjing Normal Univ Coll Geog Sci Nanjing 210097 Peoples R China

ISBN: (纸本)9780819471987

Grassland cover near Lake Qinghai in western China was mapped into mile percentage classes from a TM-derived Normalised Difference Bareness Index (NDBI) image based on 178 in situ samples collected within 1 m(2) sites. Their ground coordinates logged with a GPS unit were used to locate their pixel values on the NDBI image. A new method, in which the in situ samples and their pixel NDBI values were independently ranked prior to the establishment of their linear regression relationship, was applied to converting the NDBI image into a map of grass coverage. This relationship enabled the NDBI image to be translated into a map of grassland cover with a meaningful spatial pattern. Assessed against visually interpreted results, grassland cover was mapped at an overall accuracy of 80%. In order for this method to generate satisfactory results, image pixel NDBI values have to be normalized so that they have the same standard deviation as that of the ground samples. This proposed method should be applicable to any grassland where grassland cover varies Subtly at the pixel scale of the image used.

关键词： grassland cover remote sensing TM NDVI China

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Ant Colony Optimization Algorithm for Feature Selection and Classification of Multispectral remote sensing image

Ant Colony Optimization Algorithm for Feature Selection and ...

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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Lintao Wen Qian Yin Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China

In classification of a multispectral remote sensing image, it is usually difficult to obtain higher classification accuracy if we only consider the image's spectral feature or texture feature alone. In this paper, we present a new approach by applying the Ant Colony Optimization (ACO) algorithm to find a multi-feature vector composed of spectral and texture features in order to get a better result in the classification. The experimental results show that ACO algorithm is helpful in subset searching of the features used to classify the multispectral remote sense image. Using the combination of the spectral and texture features obtained by ACO in classification always produces a better accuracy.

关键词： Ant colony optimization remote sensing remote monitoring Multispectral imaging image analysis Data mining Feature extraction Entropy image processing pattern recognition

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Half-quadratic variational regularization methods for speckle-suppression and edge-enhancement in SAR complex image

Half-quadratic variational regularization methods for speckl...

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International conference on Earth Observation Data processing and Analysis, ICEODPA

作者： Zhao, Xia Wang, Guang-Xin Department of Mathematics and System Science National University of Defense Technology Changsha Hunan 410073 China

Synthetic aperture radar (SAR) is an active remote sensing sensor. It is a coherent imaging system, the speckle is its inherent default, which affects badly the interpretation and recognition of the SAR targets. Conventional methods of removing the speckle is studied usually in real SAR image, which reduce the edges of the images at the same time as depressing the speckle. Morever, Conventional methods lost the information about images phase. Removing the speckle and enhancing the target and edge simultaneously are still a puzzle. To suppress the spckle and enhance the targets and the edges simultaneously, a half-quadratic variational regularization method in complex SAR image is presented, which is based on the prior knowledge of the targets and the edge. Due to the non-quadratic and non- convex quality and the complexity of the cost function, a half-quadratic variational regularization variation is used to construct a new cost function,which is solved by alternate optimization. In the proposed scheme, the construction of the model, the solution of the model and the selection of the model peremeters are studied carefully. In the end, we validate the method using the real SAR *** analysis and the experimental results illustrate the the feasibility of the proposed method. Further more, the proposed method can preserve the information about images phase. © 2008 SPIE.

关键词： Synthetic aperture radar

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Change detection based on adaptive Markov Random Fields

Change detection based on adaptive Markov Random Fields

引用

International conference on pattern recognition

作者： Keming Chen Chunlei Huo Jian Cheng Zhixin Zhou Hanqing Lu National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences Beijing China

ISBN: (纸本)9781424421749

Usually changes in remote sensing images go along with the appearance or disappearance of some edges. In addition, pixels located along the edges are likely to weakly influenced by its neighborhood pixels, while pixels located far from the edges commonly have a tightly correlation among them. In this paper, we propose a novel change detection technique based on adaptive Markov Random Fields (MRFs) for high resolution satellite images with combined color and texture features. The technique is composed of two main steps: (1) the input images are marked with different region indexes by the combined color and edge features; (2) change maps are obtained under the MRF framework with alterable order of neighborhood and variable smooth weight coefficient controlled by the index map. The main contribution of this paper is that the spatial-contextual information included in the remote sensing imagery is correctly and adaptively exploited under an adaptive MRF framework. Experiments results obtained on a set of remote sensing imagery confirm the effectiveness of the proposed approach.

关键词： Markov random fields image edge detection remote sensing Histograms image resolution Satellites Spatial resolution Context modeling Laboratories image recognition

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Active contour detection of linear patterns in spectrogram images

Active contour detection of linear patterns in spectrogram i...

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International conference on pattern recognition

作者： Thomas A. Lampert Simon E. M. O'Keefe University of York UK

ISBN: (纸本)9781424421749

This paper proposes an extension to the active contour algorithm for the detection of linear patterns within remote sensing and vibration data. The proposed technique uses an alternative energy force, overcoming the limitations of the original algorithm, which relies upon simple energy formulations to extract intensity and gradient information from an image. We overcome these by forming a noise model, which is used to detect a feature¿s presence, and by integrating information from several locations within an image to strengthen the detection process.

关键词： Active contours Spectrogram Computer vision image edge detection remote sensing Signal to noise ratio Data mining Shape Convergence image converters

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Internal evaluation of registration results for radiographic images

Internal evaluation of registration results for radiographic...

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作者： Selby, Boris Peter Sakas, Gecwgios Walter, Stefan Groch, Wolf-Dkier Stilla, Uwe Medcom GmbH Rundeturmstr. 12 64283 Darmstadt Germany Fraunhofer IGD Cognitive Computing and Medical Imaging 64283 Darmstadt Germany Fachbereich Informatik University of Applied Sciences 64295 Darmstadt Germany Photogrammmetry and Remote Sensing Technische Unwersitaet Muenchen Germany

ISBN: (纸本)9781424421756

This work focuses on internal gray level based evaluation of image registration results. The motivation is to provide all approach for self-diagnosis in the scope of a patient alignment system based onl rigid registration of real and reconstructed X-ray images. As an automatic system should provide expressive indicators for the correctness of the outcome, we propose a method to eslimate the probability for the resulting transformations to lie within a predefined window of acceptable values. Based purely on image gray values, the approach is independent from previous knowledge about the images. By registration of corresponding fragments of both images we generate redundancy and define the probability density of the resulting transformations. The proposed method is tested comparing digital recondstructed radiographs (DRRs) to X-ray images. By introducing geometric and radiometric deviations we show that a reliable self-diagnosis is possible. © 2008 IEEE.

关键词： Probability density function

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GPU implementation of belief propagation using CUDA for cloud tracking and reconstruction

GPU implementation of belief propagation using CUDA for clou...

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IAPR Workshop on pattern recognition in remote sensing (PRRS)

作者： Scott Grauer-Gray Chandra Kambhamettu Kannappan Palaniappan Department of Computer and Information Sciences University of Delaware Newark DE USA Department of Computer Science University of Missouri Columbia MO USA

This paper describes an efficient CUDA-based GPU implementation of the belief propagation algorithm that can be used to speed up stereo image processing and motion tracking calculations without loss of accuracy. Preliminary results in using belief propagation to analyze satellite images of hurricane Luis for real-time cloud structure and tracking are promising with speed-ups of nearly a factor of five.

关键词： Belief propagation Costs Stereo vision Tracking Cloud computing Graphics Scattering Inference algorithms image reconstruction Computer science

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Transform methods for remote sensing environmental monitoring

Transform methods for remote sensing environmental monitorin...

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

作者： Chi Hau Chen Electrical and Computer Engineering Department University of Massachusetts Dartmouth North Dartmouth MA USA

Transform methods in signal and image processing generally speaking are easy to use and can play a number of useful roles in remote sensing environmental monitoring. Examples are the pollution and forest fire monitoring. Transform methods offer effective procedures to derive the most important information for further processing or human interpretation and to extract important features for pattern classification. Most transform methods are used for image (or signal) enhancement and compression. However other transform methods are available for linear or nonlinear discrimination in the classification problems. In this paper we will examine the major transform methods which are useful for remote sensing especially for environmental monitoring problems. Many challenges to signal processing will be reviewed. Computer results are shown to illustrate some of the methods discussed.

关键词： remote monitoring Signal processing image processing Pollution Fires Humans Data mining Feature extraction pattern classification image coding

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