Jiancheng Luo

Architecture design of grid GIS and its applications on image processing based on LAN

Computer technology and its relative subjects developed at very high speed in recent years, so is geo-information science, including Geographic Information System (GIS), remote sensing (RS) and global position system (GPS). But with the increase of data, many data cannot be used efficiently because of the tremendous amount of data and information and the difficulty of process and transfer through network. So how to develop internet technology to solve these problems becomes a difficult problem for current computer experts and geo-science experts. Fortunately, grid computing provides us the method to solve this problem effectively. Grid computing is a resources sharing model presented by computer experts to solve current network resources imbalance problem. Basing on the application of grid computing on geographical information system (GIS), this paper analyzes the weakness and problems of traditional GIS, and then gives the method to solve these problems with the technology provided by grid computing and web services. After analyzing the characteristic of grid computing this paper expatiates on current application status of grid computing on GIS and the problems it faces, with the technology of middleware, this paper presents the architecture of grid GIS and lists the techniques it needs. In conclusion, this paper concludes that the distributing middleware architecture based on grid geographic markup language (GridGML) and web services technique is a good solution to current problems, this architecture can also solve those problems such as effective resources sharing through internet and advancing international applications efficiency, at last we discuss its implementation process based on LAN.

Distributed computing model for processing remotely sensed images based on grid computing

With advances in remote-sensing technology, the large volumes of data cannot be analyzed efficiently and rapidly, especially with arrival of high-resolution images. The development of image-processing technology is an urgent and complex problem for computer and geo-science experts. It involves, not only knowledge of remote sensing, but also of computing and networking. Remotely sensed images need to be processed rapidly and effectively in a distributed and parallel processing environment. Grid computing is a new form of distributed computing, providing an advanced computing and sharing model to solve large and computationally intensive problems. According to the basic principle of grid computing, we construct a distributed processing system for processing remotely sensed images. This paper focuses on the implementation of such a distributed computing and processing model based on the theory of grid computing. Firstly, problems in the field of remotely sensed image processing are analyzed. Then, the distributed (and parallel) computing model design, based on grid computing, is applied. Finally, implementation methods with middleware technology are discussed in detail. From a test analysis of our system, TARIES.NET, the whole image-processing system is evaluated, and the results show the feasibility of the model design and the efficiency of the remotely sensed image distributed and parallel processing system

Extraction of bridges over water from IKONOS panchromatic data

Compared to remote sensing images of medium or low spatial resolution, high‐resolution remote sensing images can provide observation data containing more detailed information for georesearch. Accordingly, an important issue for current computer and geoscience experts is to develop useful methods or technology to extract information from these high‐resolution satellite images. As part of a series of research into object extraction, this paper focuses mainly on the extraction of bridges over water from high‐resolution panchromatic satellite images. Since bridges over water are obviously adjacent to water in remote sensing images, this paper proposes a practical knowledge‐based bridge extraction method for remote sensing images of high spatial resolution. The steps involved are: water extraction based on Gauss Markov Random Field (GMRF)‐Support Vector Machine (SVM) classification methods which use a SVM to classify the image based on textural features expressed by a GMRF; image thinning and removal of fragmented lines; main trunk detection by width; vectorization; and feature expression. Finally, tests are described for two pieces of panchromatic IKONOS satellite images with a 1 m resolution. The experimental results show that the proposed method is suitable for images with a single‐peak histogram (contrast between water and land is sharp) or a multi‐peak histogram (greyscale value of water is close to that of land).

System design and implementation of digital-image processing using computational grids

As a special type of digital image, remotely sensed images are playing increasingly important roles in our daily lives. Because of the enormous amounts of data involved, and the difficulties of data processing and transfer, an important issue for current computer and geo-science experts is developing internet technology to implement rapid remotely sensed image processing. Computational grids are able to solve this problem effectively. These networks of computer workstations enable the sharing of data and resources, and are used by computer experts to solve imbalances of network resources and lopsided usage. In China, computational grids combined with spatial-information-processing technology have formed a new technology: namely, spatial-information grids. In the field of remotely sensed images, spatial-information grids work more effectively for network computing, data processing, resource sharing, task cooperation and so on. This paper focuses mainly on the application of computational grids to digital-image processing. Firstly, we describe the architecture of digital-image processing on the basis of computational grids, its implementation is then discussed in detail with respect to the technology of middleware. The whole network-based intelligent image-processing system is evaluated on the basis of the experimental analysis of remotely sensed image-processing tasks; the results confirm the feasibility of the application of computational grids to digital-image processing.

Fast segmentation of high-resolution satellite images using watershed transform combined with an efficient region merging approach

High-resolution satellite images like Quickbird images have been applied into many fields. However, researches on segmenting such kind of images are rather insufficient partly due to the complexity and large size of such images. In this study, a fast and accurate segmentation approach was proposed. First, a homogeneity gradient image was produced. Then, an efficient watershed transform was employed to gain the initial segments. Finally, an improved region merging approach was proposed to merge the initial segments by taking a strategy to minimize the overall heterogeneity increased within segments at each merging step, and the final segments were obtained. Compared with the segmentation approach of a commercial software eCognition, the proposed one was a bit faster and a bit more accurate when applied to the Quickbird images.

Multiscale Water Body Extraction in Urban Environments From Satellite Images

Water is a fundamental element in urban environments, and water body extraction is important for landscape and urban planning. Remote sensing has increasingly been used for water body extraction; however, in urban environments, this kind of approaches is challenging because of the significant within-class spectral variance in water areas and the presence of complex ground features. The objective of this study is to develop an automatic method that could improve water body extraction in urban environments from moderate spatial resolution satellite images. Central to our method is the combined use of multiscale extractions and spectral mixture analysis techniques in adaptive local regions. Specifically, we first calculate the NDWI image from experimental images for selecting water sample pixels. Second, on the basis of the selected water pixels, we apply an improved spectral mixture analysis technique on the experimental image to get water abundance of every pixel, and segment the abundance image to extract water bodies at the global scale. Third, in a similar manner, we iteratively conduct the water body extraction in multiscale local regions to refine the water bodies. Finally, the final result of water bodies is obtained when a stopping criterion is satisfied. We have implemented this method to produce water maps from an ALOS/AVNIR-2 image and a Terra/ASTER image covering urban areas. The experimental results illustrate that the proposed method has substantially outperformed two related methods that use the NDWI-based thresholding and the SVM classification for the entire image.

Multi-scale information extraction from high resolution remote sensing imagery and region partition methods based on GMRF-SVM

This paper proposes the work flow of multi‐scale information extraction from high resolution remote sensing images based on features: rough classification – parcel unit extraction (subtle segmentation) – expression of features – intelligent illation – information extraction or target recognition. This paper then analyses its theoretical and practical significance for information extraction from enormous amounts of data on a large scale. Based on the spectrum and texture of images, this paper presents a region partition method for high resolution remote sensing images based on Gaussian Markov Random Field (GMRF)–Support Vector Machine (SVM), that is the image classification based on GMRF–SVM. This method integrates the advantages of GMRF‐based texture classification and SVM‐based pattern recognition with small samples and makes it convenient to utilize a priori knowledge. Finally, the paper reports tests on Ikonos images. The experimental results show that the method used here is superior to GMRF‐based segmentation in terms of both the time expenditure and processing effect. In addition, it is actually meaningful for the stage of information extraction and target recognition.

A hybrid multi-scale segmentation approach for remotely sensed imagery

The general image segmentation approach used in other domains may not be applicable to the remote sensing field, which is due to the following factors: remotely sensed data is multi-spectral, always very large in size, and in multi-scale as well. How to quickly and efficiently segment remotely sensed imagery is still a big issue to be solved. Based on human vision mechanism, a new hybrid multi-scale segmentation approach is presented, which is implemented at three coarse-to-fine scale levels. First, remotely sensed imagery is segmented at a coarse scale, and image regions (segments) are produced. Then, the corresponding regions in the original image are segmented by another segmentation approach one by one at the fine scale. From the experiment results, we found the approach is rather promising. However, there still exists some problems to be settled, and further researches should be conducted in the future.

A Mathematical Morphology Based Scale Space Method for the Mining of Linear Features in Geographic Data

This paper presents a spatial data mining method MCAMMO and its extension L_MCAMMO designed for discovering linear and near linear features in spatial databases. L_MCAMMO can be divided into two basic steps: first, the most suitable re-segmenting scale is found by MCAMMO, which is a scale space method with mathematical morphology operators; second, the segmented result at this scale is re-segmented to obtain the final linear belts. These steps are essentially a multi-scale binary image segmentation process, and can also be treated as hierarchical clustering if we view the points under each connected component as one cluster. The final number of clusters is the one which survives (relatively, not absolutely) the longest scale range, and the clustering which first realizes this number of clusters is the most suitable segmentation. The advantages of MCAMMO in general and L_MCAMMO in particular, are: no need to pre-specify the number of clusters, a small number of simple inputs, capable of extracting clusters with arbitrary shapes, and robust to noise. The effectiveness of the proposed method is substantiated by the real-life experiments in the mining of seismic belts in China.

High-precise water extraction based on spectral-spatial coupled remote sensing information

Remote sensing information extraction is the key step of remote sensing application, and the automatic and high-precise extraction of water information from remotely sensed images is of great significance and urgently required in many research fields. This paper presents a step-by-step iterative transformation mechanism to extract water information, which uses spatial scale transformation mechanism of “whole-local” based on water index fitted from spectral library using spectral angle method first, and then fuses the hierarchical knowledge of water extraction and achieves the gradually approach of the water bodys optimal margin iteratively by combining the segmentation and classification at whole and local scales respectively. Experiment of plateau lake information extraction demonstrates its better accuracy and efficiency.