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  • 标题:Automatic Bright Circular Type Oil Tank Detection Using Remote Sensing Images
  • 本地全文:下载
  • 作者:Naveen Kumar Kushwaha ; Debasis Chaudhuri ; Manish Pratap Singh
  • 期刊名称:Defence Science Journal
  • 印刷版ISSN:0976-464X
  • 出版年度:2013
  • 卷号:63
  • 期号:3
  • 页码:298-304
  • DOI:10.14429/dsj.63.2737
  • 语种:English
  • 出版社:Defence Scientific Information & Documentation Centre
  • 摘要:Automatic target detection like oil tank from satellite based remote sensing imagery is one of the important domains in many civilian and military applications. This could be used for disaster monitoring, oil leakage, etc. We present an automatic approach for detection of circular shaped bright oil tanks with high accuracy. The image is first enhanced to emphasize the bright objects using a morphological approach. Then, the enhanced image is segmented using split-and-merge segmentation technique. Here, we introduce a knowledge base strategy based on the region removal technique and spatial relationship operation for detection of possible oil tanks from the segmented image using minimal spanning tree. Lastly, we introduce a supervised classifier, for identification of oil tanks, based on the knowledge database of large amount data of oil tanks. The uniqueness of the proposed technique is that it is useful for detection bright oil tanks from high as well as low resolution images, but the technique is always better for high-resolution imagery. We have systematically evaluated the algorithm on different satellite images like IRS – 1C, IKONOS, QuickBird and CARTOSAT – 2A. The proposed technique is detected bright structures but unable to detect the dark structure. If the oil tank structures are bright relative to the background illumination in the image then the detection accuracy by the proposed technique for the high resolution image is more than 95 per cent. Defence Science Journal, 2013, 63(3), pp.298 -304 , DOI:http://dx.doi.org/10.14429/dsj. 63.2737
  • 关键词:Automatic recognition, remote sensed image, resolution, supervised technique, clustering, segmentation, minimal spanning tree
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