...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>ISPRS Journal of Photogrammetry and Remote Sensing >Segmentation of large-scale remotely sensed images on a Spark platform: A strategy for handling massive image tiles with the MapReduce model
【24h】

Segmentation of large-scale remotely sensed images on a Spark platform: A strategy for handling massive image tiles with the MapReduce model

机译：在Spark平台上分割大规模遥感图像：使用MapReduce模型处理海量图像切片的策略

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Image segmentation is essential in object-based image analysis. Numerous image segmentation algorithms have been proposed and widely applied to process remote sensing images, but most of them are designed to deal with single scenes. As the volume of images grows rapidly, handling images with single machines is becoming increasingly difficult, and the size of a composite image can be larger than the CPU memory of a single computer. To address this problem, a distributed image segmentation strategy is proposed in this paper. The two main steps of the proposed strategy are as follows. First, a prepared massive image is loaded and then decomposed into sub-images that are distributed across multiple computers; algorithms are then used in parallel to segment each sub-image into a large number of initial objects. Secondly, the proposed object resegmentation method is applied to the initial boundary objects in each sub-image in order to merge these objects. The sub-images are then ingested from the different computers in order to obtain the final segmentation image. Two classical segmentation algorithms are employed to test the proposed strategy in eight different study areas that include urban area, suburban zone and agricultural landscape. Both the intersection over union and the F-measure metrics show that the proposed strategy can help to solve the problem of the data volume being too large to fit on a single machine, and that it also performs better than comparative strategies. The proposed strategy not only has the ability to segment very large images, but also accelerates the segmentation and segmentation-based applications so that they can match the image acquisition rate.
机译：图像分割在基于对象的图像分析中至关重要。已经提出了许多图像分割算法并将其广泛地用于处理遥感图像，但是其中大多数被设计为处理单个场景。随着图像量的快速增长，使用单台计算机处理图像变得越来越困难，并且合成图像的大小可能大于单台计算机的CPU内存。为了解决这个问题，本文提出了一种分布式图像分割策略。拟议策略的两个主要步骤如下。首先，加载准备好的海量映像，然后将其分解为分布在多台计算机上的子映像。然后并行使用算法将每个子图像分割为大量初始对象。其次，将提出的对象重新分割方法应用于每个子图像中的初始边界对象，以合并这些对象。然后从不同的计算机中摄取子图像以获得最终的分割图像。两种经典的分割算法用于在八个不同的研究区域（包括市区，郊区和农业景观）中测试所提出的策略。联合交集和F度量指标均表明，所提出的策略可以帮助解决数据量太大而无法容纳在一台机器上的问题，并且其性能也优于比较策略。所提出的策略不仅具有分割非常大的图像的能力，而且还加速了基于分割和基于分割的应用程序，以便它们可以匹配图像采集速率。

著录项

  • 来源
  • 作者

  • 作者单位

    Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci 9 Dengshuang South Rd Beijing 100094 Peoples R China|Univ Chinese Acad Sci Beijing 100049 Peoples R China;

    Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci 9 Dengshuang South Rd Beijing 100094 Peoples R China|Univ Chinese Acad Sci Beijing 100049 Peoples R China|Chinese Acad Sci Aerosp Informat Res Inst State Key Lab Remote Sensing Sci Beijing 100101 Peoples R China|Chinese Acad Sci Aerosp Informat Res Inst Hainan Key Lab Earth Observat Sanya 572029 Peoples R China;

    Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci 9 Dengshuang South Rd Beijing 100094 Peoples R China;

    Chinese Acad Sci Aerosp Informat Res Inst State Key Lab Remote Sensing Sci Beijing 100101 Peoples R China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Segmentation algorithms; Distributed algorithms; Image processing; GeoTrellis; Spark;

    机译：分割算法;分布式算法;图像处理;GeoTrellis;火花;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号