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Application of Bimodal Histogram Method to Oil Spill Detection from a Satellite Synthetic Aperture Radar Image 원문보기

대한원격탐사학회지 = Korean journal of remote sensing, v.29 no.6, 2013년, pp.645 - 655  

Kim, Tae-Sung (Department of Science Education, Seoul National University) ,  Park, Kyung-Ae (Department of Earth Science Education) ,  Lee, Min-Sun (Department of Science Education, Seoul National University) ,  Park, Jae-Jin (Department of Science Education, Seoul National University) ,  Hong, Sungwook (Satellite Analysis Division, National Meteorological Satellite Center, Korea Meteorological Administration) ,  Kim, Kum-Lan (Satellite Analysis Division, National Meteorological Satellite Center, Korea Meteorological Administration) ,  Chang, Eunmi (Ziinconsulting Inc.)

Abstract AI-Helper 아이콘AI-Helper

As one of segmentation techniques for Synthetic Aperture Radar (SAR) image with oil spill, we applied a bimodal histogram method to discriminate oil pixels from non-oil pixels. The threshold of each moving window was objectively determined using the two peaks in the histogram distribution of backsca...

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제안 방법

  • In this study, we applied the bimodal histogram method to an ENVISAT ASAR image with oil spills which satisfied the limit of wind speed, and then compared the results with those from the previous method.
  • 1). The SAR data was utilized to apply the oil spill detection methods and investigate the characteristics of each detectionmethod. Details of the ENVISAT ASAR image used in this study were summarized in Table 1.
  • The objectives of this study are (1) to apply the adaptive threshold method algorithm to detect oilspills from an ENVISAT SAR image, (2) to develop the bimodal histogram method based on the Gaussian fittingmethod, and (3)to compare the twomethods and present some differences.
  • Thus, a more robust method capable of determining the threshold objectively and automatically for operational purposesis necessary. Therefore, in this study, we introduce the bimodal histogram method for oil spill detection from SAR images with a single polarization, and then compare this method with the adaptive threshold method.
  • Using the NRCS, the adaptive threshold in a moving window was estimated and then low backscattering pixels regarded as dark spots were separated according to the threshold. To better separate the spill from its surroundings and estimate the extent ofthe oil-covered area, the detected dark spots were clustered using connectivity in eight neighbourhood directions. Based on the estimated extents, clusters of dark spots which were smaller than the minimum cluster size were assumed to be look-alikes and discriminated.

대상 데이터

  • Therefore, we searched European Space Agency’s(ESA) quick-look images of the world’s oceans and seas and collected SAR images which contained oil-covered areas. Among them, we selected the SAR image from ENVISAT Advanced Synthetic Aperture Radar (ASAR) off the coast of Libya in the Mediterranean Sea which showed obvious dark patches originated from natural oil seeps (Fig. 1). The SAR data was utilized to apply the oil spill detection methods and investigate the characteristics of each detectionmethod.
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