remotesensingimages acquired by the sensors at platforms near land surface, airplane and satellite, usually have large volume and miscellaneous data formats. So it is not feasible for the users to browse remote sens...
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ISBN:
(纸本)081944684X
remotesensingimages acquired by the sensors at platforms near land surface, airplane and satellite, usually have large volume and miscellaneous data formats. So it is not feasible for the users to browse remotesensingimages and evaluate the quality of images and select the suitable images on Internet. Moreover, it is inefficient to read and transfer remotesensingimages real-timely in a standard image viewer due to their miscellaneous data formats. In order to clear up the problems, the metadata and microimage are extracted from various remotesensingimages, managed by the database management system software, and browsed and evaluated on Internet to decide which images are the real wanted. The process of working includes the 4 steps. 1) Create metadata for the remotesensingimages. The metadata consist of image data format, longitude and latitude of image range, date and, time, spatial resolution, sensor attributes (field of view, bands, performance and precision etc), platform attributes (stand near land surface, airplane or satellite), flight path or orbit attributes of aerial and space observation etc. 2) Create microimage for remotesensingimage. Firstly, the remotesensingimages are projected to the same coordinate system by the geometric correction, so all images can be matched correctly. Then the microimages are built through 1: 10 or 1:5 cubic convolution sampling the corrected images. 3). Build a database to store and manage the metadata and microimages, and create pointers to hyperlink the remotesensingimages self. 4) Develop the browse interface, publish the remotesensingimage base on Internet, and receive the users' order forms. The wanted images will be sent on CDROM if the orders are accepted. The interface is visualized. Here, a color spectrum is used to express the bands. A clock is for time and landscape is for days in one year. And place is located by moving your mouse on the map. The pixel sizes are shown through levels on a pyramid.
Change detection is a key topic in land use/land cover related studies and significant efforts have been made in the development of methods for change detection. In this article a multivariate analysis method based on...
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ISBN:
(纸本)081944684X
Change detection is a key topic in land use/land cover related studies and significant efforts have been made in the development of methods for change detection. In this article a multivariate analysis method based on canonical transformation is introduced into change detection using multi-temporal remotesensingimageries. Afterwards an automatic unsupervised discriminating technique based on the Bayes-Rule of Minimum Error is employed for changed areas identification in the difference image. Experimental results of a case study using Landsat TM imageries are presented to demonstrate the effectiveness of our method.
Observation by limited field observational stations is the major conventional method of research on snow and relevant problem, such as flood caused by melting of snow. However, observation on only several ground obser...
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ISBN:
(纸本)0819451819
Observation by limited field observational stations is the major conventional method of research on snow and relevant problem, such as flood caused by melting of snow. However, observation on only several ground observatories cannot provide enough information for large scale region accurately and timely. The advanced technology of remotesensing and geographic information system (GIS) is an effective tool to extract snow information and monitor snow change quickly and dynamically. This paper discusses the method of snow mapping and establishing dynamic snow monitoring information system by using multisensor, multispectral and multitemporal remotesensing data ( NOAA/AVHRR, satellite borne SAR and TM). The study results show that the use of muitisensor remote sensed data and technique of GIS, combined with relative contemporaneous field observational data, enables the snow monitoring more rapid and accurate.
A novel unsupervised classification scheme called spatial fuzzy C-means clustering is proposed in this article. Based oil conventional fuzzy C-means algorithm, our scheme takes spatial homogeneity into consideration b...
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ISBN:
(纸本)0819451819
A novel unsupervised classification scheme called spatial fuzzy C-means clustering is proposed in this article. Based oil conventional fuzzy C-means algorithm, our scheme takes spatial homogeneity into consideration by introducing spatial membership and applying SMNF, thus improved robustness against noises or outliers. Preliminary experimental results are also shown to demonstrate effectiveness of our method.
SAR image segmentation can be converted to a clustering problem in which pixels or small patches are grouped together based on local feature information. In this paper, we present a novel framework for segmentation. T...
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ISBN:
(纸本)081944684X
SAR image segmentation can be converted to a clustering problem in which pixels or small patches are grouped together based on local feature information. In this paper, we present a novel framework for segmentation. The segmentation goal is achieved by unsupervised clustering upon characteristic descriptors extracted from local patches. The mixture model of characteristic descriptor, which combines intensity and texture feature, is investigated. The unsupervised algorithm is derived from the recently proposed Skeleton-Based Data Labeling method. Skeletons are constructed as prototypes of clusters to represent arbitrary latent structures in image data. Segmentation using Skeleton-Based Fuzzy Clustering is able to detect the types of surfaces appeared in SAR images automatically without any user input.
With urban and township development and E-Government program promotion in China city remotesensing as base data has developed rapidly. The technique demands in accuracy and effective edge detection and extraction fro...
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ISBN:
(纸本)0819451819
With urban and township development and E-Government program promotion in China city remotesensing as base data has developed rapidly. The technique demands in accuracy and effective edge detection and extraction from higher resolution image become important focal area. In the current popular imageprocessing software packages there are some existing edge detection convolution kernels such as Sobel, Robert, Prewitt, Kirsch, Gauss-Laplace kernels. In general the kernels all work based on algorithm of convolution kernel in spatial territory of the image. However, satellite sensors capture spatial and spectral signatures of surface at same time. Use of both spatial and spectral features to establish a edge detection process is a new notion for achieving more accuracy results. In the paper we introduce a spatial and spectral integrated method which is designed in four stages. The result suggests that four stages process can achieve more cleanly and accuracy edges of city constructions than that results of using other algorithms. The procedure is summarized in figure 1.
remotesensing data, especially the hyperspectral remotesensing data, characterize their great quantities. So how to deal with these data is a focus. Database has solved the problem of storing, searching, updating an...
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ISBN:
(纸本)0819451819
remotesensing data, especially the hyperspectral remotesensing data, characterize their great quantities. So how to deal with these data is a focus. Database has solved the problem of storing, searching, updating and maintaining of the data, but it is not satisfactory in disposing them. In recent years, the technology of data warehouse has great development. It can re-integrate, synthesize and separate the data of database, and use the searching pattern of multiple dimensions to realize data mining(DM). This technology has been widely used in commerce to analyze the inner relationship of the numerous data and makes some remarkable achievements in decision supporting. Data warehouse and Data mining technology have been used in GIS. This article would give a set of complete steps and some general methods in using the DM to analyze the remotesensing data, especially in hyperspectral data. And it tries to do some preliminary exploration in using it to deeply analyze the potential relations among the acquired spectra, images and biology parameters of the experiments and get some anticipated possible results.
In this paper, we propose a novel and automated line extraction algorithm in multi-spectral images, which fully utilize the complementary information among multi-spectral images. It consists of three main aspects: Fir...
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ISBN:
(纸本)081944684X
In this paper, we propose a novel and automated line extraction algorithm in multi-spectral images, which fully utilize the complementary information among multi-spectral images. It consists of three main aspects: Firstly, edges are extracted from every spectral image. Then, the edge points from all spectral images are grouped into combined line-support regions according to certain fusion rules. Finally, fits the regions and generates the fused lines. The new algorithm is applied to some real multi-spectral images. The experimental results show that the new algorithm is effective.
In this paper, a novel hierarchical image fusion scheme based on wavelet multi-scale decomposition is presented. The basic idea is to perform a wavelet multi-scale decomposition of each source image first, then the wa...
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ISBN:
(纸本)081944684X
In this paper, a novel hierarchical image fusion scheme based on wavelet multi-scale decomposition is presented. The basic idea is to perform a wavelet multi-scale decomposition of each source image first, then the wavelet coefficients of the fused image is constructed using region-based selection and weighted operators according to different fusion rules, finally the fused image is obtained by taking inverse wavelet transform. Ibis approach has been successfully used in image fusion. In addition, with the use of the parameters such as entropy, cross entropy, mutual information, root mean square error, peak-to-peak signal-to-noise ratio, the performance of the fusion scheme is evaluated and analyzed. The experimental results show that the fusion scheme is effectual.
Contextual classification methods, which require the extraction of complex spatial information over a range of scales, from fine details in local areas to large features that extend across the image, are necessary in ...
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ISBN:
(纸本)081944667X
Contextual classification methods, which require the extraction of complex spatial information over a range of scales, from fine details in local areas to large features that extend across the image, are necessary in many remotesensingimage classification studies. This work presents a supervised adaptive object recognition model which integrates scale-space filtering techniques for feature extraction within a neural classification procedure based on multilayer perceptron (MLP). The salient aspect of the model is the integration within the back-propagation learning task of the search of the most adequate filter parameters. The experimental evaluation of the method has been conducted coping with object recognition in high-resolution remotesensingimagery. To investigate whether the strategy can be considered an alternative to conventional procedures the results were compared with those obtained by a well known contextual classification scheme.
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