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地球科学与环境学报 2006年第1期28卷 75-78,100页

作者：林卉杜培军肖剑平徐州师范大学国土信息与测绘工程系江苏徐州221116 中国矿业大学地理信息科学系江苏徐州221008 武汉市勘测设计研究院湖北武汉430022

在分析了小波变换的分解与重建方法后,提出了一种基于区域的图像增强算法。先提取出源图的边缘,以图像的边缘为参考,围绕边缘建立融合窗口,然后结合区域内的图像信息,应用基于窗口的融合规则进行融合处理。实验结果显示,融合后的图像综... 详细信息

在分析了小波变换的分解与重建方法后,提出了一种基于区域的图像增强算法。先提取出源图的边缘,以图像的边缘为参考,围绕边缘建立融合窗口,然后结合区域内的图像信息,应用基于窗口的融合规则进行融合处理。实验结果显示,融合后的图像综合了3幅源图像的不同特征,处理后的图像变得容易识别了。表明该方法保持了尽可能多的原始信息,算法简单,稳定性好,适合于多光谱遥感图像识别、医学成像等领域。

关键词：图像融合小波变换分解与重建区域

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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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Implementation of real-time constrained linear discriminant analysis to remote sensing image classification

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pattern recognition 2005年第4期38卷 459-471页

作者： Du, Q Nekovei, R Mississippi State Univ Dept Elect & Comp Engn Mississippi State MS 39762 USA Texas A&M Univ Dept Elect Engn & Comp Sci Kingsville TX 78363 USA

In this paper, we investigate the practical implementation issues of the real-time constrained linear discriminant analysis (CLDA) approach for remotely sensed image classification. Specifically, two issues are to be resolved: (1) what is the best implementation scheme that yields lowest chip design complexity with comparable classification performance. and (2) how to extend CLDA algorithm for multispectral image classification. Two limitations about data dimensionality have to be relaxed. One is in real-time hyperspectral image classification. where the number of linearly independent pixels received for classification must be larger than the data dimensionality (i.e., the number of spectral bands) in order to generate a non-singular sample correlation matrix R for the classifier, and relaxing this limitation can help to resolve the aforementioned first issue. The other is in multispectral image classification. where the number of classes to be classified cannot be greater than the data dimensionality, and relaxing this limitation can help to resolve the afore mentioned second issue. The former can be solved by introducing a pseudo inverse initiate of sample correlation matrix for R-1 adaptation. and the latter is taken care, of by expanding the data dimensionality via the operation of hand multiplication. Experiments on classification performance using these modifications are conducted to demonstrate their feasibility. All these investigations lead to a detailed ASIC chip design-scheme for the real-time CLDA algorithm suitable to both hyperspectral and multispectral images. The proposed techniques. to resolving these two dimensionality limitations are instructive to the real-time implementation of several popular detection and classification approaches in remote sensing image exploitation. (C) 2004 pattern recognition Society. Published by Elsevier Ltd. All rights reserved.

关键词： real-time processing constrained linear discriminant analysis (CLDA) classification hyperspectral imagery imagery

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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...

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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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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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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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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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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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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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