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Computer-Assisted Analysis of Near-Bottom Photos for Benthic Habitat Studies

机译:底栖栖息地研究的近底照片的计算机辅助分析

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This paper reports on a methodology developed for the analysis of near-bottom photographs collected for fisheries habitat studies. These tools provide a framework for conducting minimally invasive in-situ investigations of benthic organism abundance, diversity, and distribution using high-resolution optical datasets integrated with high precision navigational data. Utilizing these techniques with near-bottom photos collected with a precision navigated survey platform greatly increases the efficiency of image analysis and provides new insight about the relationships between benthic organisms and the habitats in which they are found. Basic requirements for the analysis of near-bottom seafloor images include camera calibration and quantification of the height of the lens above the seafloor throughout the survey. Corrections are required to compensate for image distortion due to lighting limitations and the variable micro-topography of the seafloor. These parameters can be constrained by utilizing precisely navigated survey platforms such as Autonomous Underwater Vehicles (AUVs) or Remote Operated Vehicles (ROVs). The methodology we present was developed with data collected by the SeaBED AUV off the coast of Washington, Oregon and California [1]. A digital database containing benthic organism identifications, measurements, and locations was generated for each image using a Graphical User Interface (GUI) created in Matlab(tm) [1,2]. This methodology has demonstrated a significant increase in the efficiency of image analysis for benthic habitat studies, and provides the opportunity to assess small scale spatial distribution of organisms in their natural habitats. Collecting overlapping images permits the creation of photomosaics [3] and the quantification of organism abundance per unit area of the seafloor.
机译:这种科学的方法本文报道的收集渔业栖息地研究近底照片的分析开发。这些工具用于进行微创提供一个框架,在原位使用具有高精确度的导航数据集成高分辨率光学数据集底栖生物丰度,多样性和分布的调查。利用这些技术与精密导航调查平台收集的近底照片大大提高了图像分析的效率,并提供有关底栖生物和它们所发现的栖息地之间的关系的新见解。对于近底海底图像分析的基本要求包括摄像机标定和整个调查海底上镜头的高度量化。校正需要用于补偿图像失真由于光线限制和海底的可变微地形。这些参数可通过利用精确导航调查平台如自主式水下航行器(AUV)或远程操作潜水器(ROV)的约束。该方法我们目前是由海底AUV收集过华盛顿州,俄勒冈州和加利福尼亚州[1]的海岸数据开发的。用于使用图形用户界面(GUI)在Matlab(TM)创建的[1,2]的每个图像产生含有底栖生物识别,测量和位置的数字数据库中。这种方法已经证明图像分析的底栖生境研究的效率显著增加,提供了机会,以评估它们的自然栖息地的生物小规模的空间分布。收集重叠图像允许photomosaics的创建[3]和生物体丰每海底的单位面积的定量。

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