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  • 1
    Publication Date: 2016-01-01
    Description: Due to the increasing dimensionality and volume of remotely sensed hyperspectral data, the development of acceleration techniques for massive hyperspectral image analysis approaches is a very important challenge. Cloud computing offers many possibilities of distributed processing of hyperspectral datasets. This paper proposes a novel distributed parallel endmember extraction method based on iterative error analysis that utilizes cloud computing principles to efficiently process massive hyperspectral data. The proposed method takes advantage of technologies including MapReduce programming model, Hadoop Distributed File System (HDFS), and Apache Spark to realize distributed parallel implementation for hyperspectral endmember extraction, which significantly accelerates the computation of hyperspectral processing and provides high throughput access to large hyperspectral data. The experimental results, which are obtained by extracting endmembers of hyperspectral datasets on a cloud computing platform built on a cluster, demonstrate the effectiveness and computational efficiency of the proposed method.
    Print ISSN: 1058-9244
    Electronic ISSN: 1875-919X
    Topics: Computer Science , Media Resources and Communication Sciences, Journalism
    Published by Hindawi
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  • 2
    Publication Date: 2019-10-01
    Description: The abrasion of stilling basin slabs which is caused by waterborne particles is one of the main surface damages in the operation of hydropower station. For determining whether to repair the stilling basin slabs, periodic inspections of erosion condition of stilling basin slabs are required. The practical problem is how to get the underwater image without unwatering and how to analyse the abrasion though the images. This paper developed a novel underwater inspection system named UIS-1 which consists of a customized underwater robot and special quantitative analysis method for this situation. Firstly, the integrated component was designed for the underwater robot that partially removes the siltation and obtains the image of the concrete surface of stilling basin slabs in the desired position. Secondly, the paper proposed an image algorithm to obtain aggregate exposure ratio for quantitative abrasion analysis. This image algorithm used SLIC superpixel and the SVM machine learning method to detect the coarse aggregate exposure automatically. Then, the aggregate exposure ratio was calculated to analyse the degree of abrasion. Finally, the UIS-1 system was evaluated in the field experiments of a dam in Sichuan, China, and its performance was discussed by comparison.
    Print ISSN: 1687-8086
    Electronic ISSN: 1687-8094
    Topics: Architecture, Civil Engineering, Surveying
    Published by Hindawi
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