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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471-480 订阅

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Exploitation of complementary sensors for precise ground-registration of sensed objects

Exploitation of complementary sensors for precise ground-reg...

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conference on image processing and pattern recognition in remote sensing ii

作者： Cook, G Kansal, S George Mason Univ ECE Dept Fairfax VA 22030 USA

ISBN: (纸本)0819456187

In remote sensing, one is often interested in not only ascertaining the presence of certain resources or objects of interest, but also in determining their locations. Ground registration involves locating the target in sensor coordinates and performing a series of coordinate transformations to convert this location to earth coordinates. One application of this would be in preparing a scaled map showing the precise locations of the resources/objects of interest. To improve ground registration accuracy one can combine multiple looks from a single sensor and/or looks from multiple sensors. One advantage in utilizing multiple sensors is that one can fuse the measurements in such a way as to exploit the best characteristics of each sensor. This paper is applied to vehicular mounted remote sensing and presents the benefits obtained when combining radar and IR as a means of determining ground coordinates of the objects of interest.

关键词： G eo-registration sensor-fusion complementary-sensors regions-of-confidence multi-look processing

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Mapping accuracy via spectral and structural based filtering techniques, comparisons through visual observations.

Mapping accuracy via spectral and structural based filtering...

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conference on image processing and pattern recognition in remote sensing ii

作者： Chockalingam, L Faculty of Information Science and Technology Multimedia University Malaysia

ISBN: (纸本)0819456187

The data of Gunung Ledang region of Malaysia acquired through LANDSAT are considered to map certain hydrogeological features. To map these significant features, image-processing tools such as contrast enhancement, edge detection techniques are employed. The advantages of these techniques over the other methods are evaluated from the point of their validity in properly isolating features of hydrogeological interest are discussed. As these techniques take the advantage of spectral aspects of the images, these techniques have several limitations to meet the objectives. To discuss these limitations, a morphological transformation, which generally considers the structural aspects rather than spectral aspects from the image, are applied to provide comparisons between the results derived from spectral based and the structural based filtering techniques.

关键词： spectral structural morphology Landsat hydrogeology thematic map remote sensing data erosion dilation opening closing

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Segmentation of LANDSAT TM image using the Markov Random Field model toward a category classification of higher accuracy

Segmentation of LANDSAT TM image using the Markov Random Fie...

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conference on image processing and pattern recognition in remote sensing ii

作者： Kawaguchi, S Yamazaki, K Tokyo Gakugei Univ Tokyo 1848501 Japan

ISBN: (纸本)0819456187

In category classification of remotely sensed imagery, it is important that pixels of image are classified using spatial informaton. We have implemented MRF(Markov Random Field) model for a classification of higher accuracy. The model of MRF is a random field whose random variable is owed to its neighborhood. The LANDSAT TM data of the Kanto area, Japan, has been alalyzed with the manner of iteration in which probability density function for a confiuration of classes reaches a maximum. Partly because of taking into account of edge information in image, the results show considerably good classification.

关键词： Markov Random Field model clustering method LANDSAT TM image edge information suburbanized area

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Integration of remote sensing, GIS and GPS techniques for dynamic monitoring of land resources in mountainous areas - A case study of Renhe District, Sichuan, China

Integration of Remote Sensing, GIS and GPS techniques for dy...

引用

conference on image processing and pattern recognition in remote sensing ii

作者： Wang, ZY Chen, XW Zhao, SH Luo, YH Peking Univ Inst Remote Sensing & GIS Beijing Peoples R China

ISBN: (纸本)0819456187

Geometric and radiometric correction, image processing, information extraction and the integration of remote sensing, GIS and GPS in the specific approach for dynamic monitoring of land resources in mountainous areas are discussed. A synthesized method combining the image difference approach with comparison post classification is employed and a monitoring system based on remote sensing, GIS and GPS are set up. Different illumination conditions are key factors influencing the spectral features in mountainous areas, thus the comprehensive analysis of DEM and NDVI are employed to restrain the influence of terrain. Errors also commonly generate in the registration of different temporal images and much change information is usually lost when the mean-value smoothing template is employed in the image processing in mountainous areas. To reduce the information lost, a regional auto-adaptive smoothing template is employed. As a case study, according to the specific characteristics of mountainous areas, the TM images acquired from both 1994 and 1996 are processed for land change detection in Renhe District, Sichuan. Field experiments for radiometric correction are conducted in the areas of 25 Km(2) in this district. The changed areas are precisely surveyed and validated after the fieldwork in which the database of detailed land survey is acquired. Combined with Geological Information System (GIS) technology and Global Position System (GPS), a 3S-based dynamic monitoring system of land resources change information in Renhe District is established, which helps the data renewal and daily management. Finally, the key factors influencing the accuracy of information extracting in mountainous areas are discussed.

关键词： land use/cover change remote sensing Geographical Information System mountainous areas

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IEEE1394 and entropy reduction techniques as applied to remote sensing spacecraft communication systems

IEEE1394 and entropy reduction techniques as applied to remo...

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conference on image processing and pattern recognition in remote sensing ii

作者： Masini, P Julian, RL Jensen, KL Raytheon Co. (United States)

ISBN: (纸本)0819456187

This paper describes work being done at Raytheon-Santa Barbara remote sensing (SBRS) in the area of entropy reduction of remote sensing data on the National Polar-Orbiting Operational Environmental Satellite System (NPOESS) Visible/Infrared imager/Radiometer Suite (ViiRS) instrument. The ViiRS instrument will produce the largest amount of data on the NPOESS satellite platform, and thus has the greatest impact on data rate. The ViiRS instrument produces 22 bands of radiometric and imaging data, which must be transmitted to the spacecraft without loss of data integrity. ViiRS uses an implementation of the RICE algorithm, along with spectral subtraction and data trimming that are described in this paper, to provide lossless data compression. This paper will also describe a simulation that predicted the data reduction performance and the resulting sensor data rates when ViiRS observes the earth from orbit. This paper will also describe the ViiRS implementation of the Fault Tolerant 1394 data network that utilizes the 1394 ASIC chipset developed by the NPOESS Integrated Program Office (IPO) and Northrop Grumman Space and Technology (NGST). This high-speed network will facilitate the reliable transmission of large amounts of compressed and uncompressed science and telemetry data from the ViiRS instrument to the NPOESS spacecraft.

关键词： Sensors Space operations Electronics Data compression Data processing Satellites MODIS remote sensing Simulation of CCA and DLA aggregates Staring arrays

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Removing vegetation using unsupervised fully constrained least squares linear spectral mixture analysis method in soils surveying by remote sensing

Removing vegetation using unsupervised fully constrained lea...

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conference on image processing and pattern recognition in remote sensing ii

作者： Luo, HX Ye, HZ Ke, YH Pan, JP Gong, JY Chen, XL SW Normal Univ Sch Resources & Environm Chongqing 400715 Peoples R China

ISBN: (纸本)0819456187

In the region covered by variable amounts of vegetation, pixel spectra received by remotely-sensed sensor with given spatial resolution are a mixture of soil and vegetation spectra, so vegetation covering on soil influences the accuracy of soils surveying by remote sensing. Mixed pixel spectra are described as a linear combination of endmember signature matrix with appropriate abundance fractions correspond to it in a linear mixture model. According to the principle of this model, abundance fractions of endmembers in every pixel were calculated using unsupervised fully constrained least squares(UFCLS) algorithm. Then the signature of vegetation correspond to its abundance fraction was eliminated, and other endmember signatures covered by vegetation were restituted by scaling their abundance fractions to sum the original pixel total and recalculating the model. After above processing, de-vegetated reflectance images were produced for soils surveying. The accuracies of paddy soils classified using these characteristic images were better than that of using the raw images, but the accuracies of zonal soils were inferior to the latter. Compared to many other image processing methods, such as K-T transformation and ratio bands, the linear spectral unmixing to removing vegetation produced slightly better overall accuracy of soil classification, so it was useful for soils surveying by remote sensing.

关键词： soils surveying classification of remote sensing image linear spectral mixture model fully constrained least squares (FCLS) algorithm unsupervised FCLS (UFCLS) method

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Landsat TM multi-spectral classification using support vector machine method in low-hill areas

Landsat TM multi-spectral classification using support vecto...

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conference on image processing and pattern recognition in remote sensing ii

作者： Zhao, SH Ke, CQ Dong, XQ Li, JL Feng, XZ Peking Univ Inst Remote Sensing & GIS Beijing 100871 Peoples R China

ISBN: (纸本)0819456187

Support vector machine (SVM) is a newly learning machine. In the paper, it applied the SVM method to research on remote sensing multi-spectral classification using Landsat TM data. It selected the typical low-hill area as study site, which was located on the southern of the Yangze River, China. The land cover types were divided into six categories, which were the waterbody, the construction land, the paddy field, the woodland, the teagarden, and the bare land, etc. The classification of the study site using the Kohonen networks method was also given. The classification results show that classification accuracy of the SVM method is better than that of the Kohonen Networks method. Especially it could discriminate the woodlands from the mountainous shadow. In conclusion, the SVM method could gain higher classification accuracy using smaller training sample in low-hill area. It could also solve the confusion problems among the ground objects.

关键词： multi-spectral classification SVM Kohonen Networks low-hill area

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recognition of image with natural textures based on learning of information augmentation

Recognition of image with natural textures based on learning...

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2nd International conference on Intelligent Information processing

作者： Cheng, XY Yuan, XH Li, SQ Xian, DS Nanjing Univ Sci & Technol Dept Comp Sci Nianjing 21904 Peoples R China

ISBN: (纸本)038723151X

The efficiency of pattern recognition depends heavily on that if feature extraction and selecting are effective. Complicated image such as medical image and remote sensing image, belong to image with natural textures, this kind of image is always of high resolution, with many layers of gray degree, and a very intricate shape structure. Because there are no obvious shapes, but only distributions of some gray degrees and colors in these images, so for them, there are no good methods yet for feature extraction and region recognition. In this paper, based on information augmentation and kinetics, we present a learning algorithm, which can be used to do region classification of the above-mentioned images with natural textures. We applied our algorithm to recognition of image with natural textures and obtained a good result.

关键词： pattern recognition information entropy augmentation machine learning NN

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Comparison of pixel-based fusion between multi-spectral data and high spatial resolution data

Comparison of pixel-based fusion between multi-spectral data...

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conference on image processing and pattern recognition in remote sensing ii

作者： Zhao, SH Wang, ZY Dong, XQ Chen, XW Peking Univ Inst Remote Sensing & GIS Beijing 100871 Peoples R China

ISBN: (纸本)0819456187

In the paper, experiments and analysis of three pixel-based fusion methods had been discussed. The fusion methods include IHS, PCA and Brovey transform method. The fusion experiments were carried out in two circs, that is, between Landsat TM multi-spectral data and SPOT-4 Pan data, Landsat TM multi-spectral data and IRS-C Pan data. From the fusion results, the definition of all fusion images were improved greatly compared to the Landsat TM image. Especially the linear ground objects are much clear, such as the roads, the residents, the bridges, etc. According to the fusion between Landsat TM data and SPOT-4 Pan data, the Brovey fusion method was the best one. The PCA fusion method was better than the IHS fusion method. According to the fusion between Landsat TM data and IRS-C Pan data, the Brovey fusion method was also the best one. But the IHS fusion method was better than the PCA fusion method. Maximum likelihood method of classification was carried out on the fusion result, and classification accuracy of the classification results were evaluated. From the evaluation result, it can be concluded that classification accuracy of the Brovey fusion result is the highest between Landsat TM data and IRS-C Pan data. Classification accuracy of the IHS fusion result is the highest between Landsat TM data and SPOT-4 Pan data.

关键词： pixel-based fusion Brovey fusion method PCA IHS classification accuracy

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A band-weighted landuse classification method for multispectral images

A band-weighted landuse classification method for multispect...

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conference on Computer Vision and pattern recognition

作者： Pan, CH Wu, G Prinet, V Yang, Q Ma, SD Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100864 Peoples R China

ISBN: (纸本)0769523722

Landuse classification is an important problem in the remote sensing field. It can be used in a wide range of applications. In this paper we propose a hybrid method fusing edges and regions information for the landuse classification of multispectral images. It mainly includes the steps of image pre-processing, initial segmentation and region merging. Especially, a novel spatial mean shift procedure is proposed so that some information can be extracted and used in the successive steps. Aiming at the multispectral images processing, we also design a band weighting strategy that give a proper weight to each band adaptively according to the region to be processed. Experimental results on the Landsat TM and ETM+ images validate the performance of the proposed method.

关键词： pattern recognition

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