首页> 外文期刊>International journal of applied mechanics >Comparison of True-Color and Multispectral Unmanned Aerial Systems Imagery for Marine Habitat Mapping Using Object-Based Image Analysis
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Comparison of True-Color and Multispectral Unmanned Aerial Systems Imagery for Marine Habitat Mapping Using Object-Based Image Analysis

机译:基于对象的图像分析的真实颜色和多光谱无人空中系统图像对海洋栖息地映射的比较

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The use of unmanned aerial systems (UAS) over the past years has exploded due to their agility and ability to image an area with high-end products. UAS are a low-cost method for close remote sensing, giving scientists high-resolution data with limited deployment time, accessing even the most inaccessible areas. This study aims to produce marine habitat mapping by comparing the results produced from true-color RGB (tc-RGB) and multispectral high-resolution orthomosaics derived from UAS geodata using object-based image analysis (OBIA). The aerial data was acquired using two different types of sensors-one true-color RGB and one multispectral-both attached to a UAS, capturing images simultaneously. Additionally, divers' underwater images and echo sounder measurements were collected as in situ data. The produced orthomosaics were processed using three scenarios by applying different classifiers for the marine habitat classification. In the first and second scenario, the k-nearest neighbor (k-NN) and fuzzy rules were applied as classifiers, respectively. In the third scenario, fuzzy rules were applied in the echo sounder data to create samples for the classification process, and then the k-NN algorithm was used as the classifier. The in situ data collected were used as reference and training data. Additionally, these data were used for the calculation of the overall accuracy of the OBIA process in all scenarios. The classification results of the three scenarios were compared. Using tc-RGB instead of multispectral data provides better accuracy in detecting and classifying marine habitats when applying the k-NN as the classifier. In this case, the overall accuracy was 79%, and the Kappa index of agreement (KIA) was equal to 0.71, which illustrates the effectiveness of the proposed approach. The results showed that sub-decimeter resolution UAS data revealed the sub-bottom complexity to a large extent in relatively shallow areas as they provide accurate information that permits the habitat mapping in extreme detail. The produced habitat datasets are ideal as reference data for studying complex coastal environments using satellite imagery.
机译:在过去几年中使用无人机的空中系统(UAS)由于其敏捷性和能够将区域与高端产品而言而言而爆炸。 UAS是近距离感应的低成本方法,使科学家的高分辨率数据具有有限的部署时间,即使是最无法访问的区域也是如此。本研究旨在通过比较来自基于对象的图像分析(OBIA)的真正彩色RGB(TC-RGB)和来自UAS地理数据的多光谱高分辨率正骨骼产生的结果来产生海洋栖息地映射。使用两种不同类型的传感器获取空中数据 - 一个真彩RGB和一个多光谱 - 两个连接到UA,同时捕获图像。另外,将潜水员的水下图像和回声发声器测量作为原位数据收集。通过对海洋栖息地分类应用不同的分类器来使用三种情况处理所产生的正骨。在第一和第二场景中,k最近邻(k-nn)和模糊规则分别作为分类器应用。在第三种情况下,在回声发声器数据中应用模糊规则以创建分类过程的样本,然后使用K-NN算法作为分类器。收集的原位数据被用作参考和培训数据。此外,这些数据用于计算所有场景中OBIA过程的整体准确性。比较了三种情况的分类结果。使用TC-RGB而不是多光谱数据,在将K-NN施加为分类器时,在检测和分类海洋栖息地提供更好的准确性。在这种情况下,总体准确性为79%,喀布巴协议指数(起亚)等于0.71,这表明了所提出的方法的有效性。结果表明,子二次计分辨率UAS数据在很大程度上在相对较浅的区域中显示了次底复杂度,因为它们提供了允许以极端细节施加栖息地映射的准确信息。所生产的栖息地数据集是使用卫星图像研究复杂沿海环境的参考数据。

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