Abstract Traditionally, the multiple images collected by cameras mounted on Unmanned Aircraft Systems (UAS) are mosaicked into a single orthophoto on which Object-Based Image Analysis (OBIA) is conducted. This approach does not take advantage of the Multi-View (MV) information of the individual images. In this study, we introduce a new OBIA approach utilizing multi-view information of original UAS images and compare its performance with that of traditional OBIA, which uses only the orthophoto (Ortho-OBIA). The proposed approach, called multi-view object-based image analysis (MV-OBIA), classifies multi-view object instances on UAS images corresponding to each orthophoto object and utilizes a voting procedure to assign a final label to the orthophoto object. The proposed MV-OBIA is also compared with the classification approaches based on Bidirectional Reflectance Distribution Function (BRDF) simulation. Finally, to reduce the computational burden of multi-view object-based data generation for MV-OBIA and make the proposed approach more operational in practice, this study proposes two window-based implementations of MV-OBIA that utilize a window positioned at the geometric centroid of the object instance, instead of the object instance itself, to extract features. The first window-based MV-OBIA adopts a fixed window size (denoted as FWMV-OBIA), while the second window-based MV-OBIA uses an adaptive window size (denoted as AWMV-OBIA). Our results show that the MV-OBIA substantially improves the overall accuracy compared with Ortho-OBIA, regardless of the features used for classification and types of wetland land covers in our study site. Furthermore, the MV-OBIA also demonstrates a much higher efficiency in utilizing the multi-view information for classification based on its considerably higher overall accuracy compared with BRDF-based methods. Lastly, FWMV-OBIA and AWMV-OBIA both show potential in generating an equal if not higher overall accuracy compared with MV-OBIA at substantially reduced computational costs.

中文翻译：

---

使用无人机系统图像的基于多视图对象的湿地土地覆盖分类

摘要 传统上，由安装在无人机系统 (UAS) 上的摄像机采集的多幅图像拼接成单个正射影像，在该正射影像上进行基于对象的图像分析 (OBIA)。这种方法没有利用单个图像的多视图 (MV) 信息。在这项研究中，我们引入了一种新的 OBIA 方法，利用原始 UAS 图像的多视图信息，并将其性能与仅使用正射影像 (Ortho-OBIA) 的传统 OBIA 的性能进行比较。所提出的方法称为基于多视图对象的图像分析 (MV-OBIA)，它对对应于每个正射影像对象的 UAS 图像上的多视图对象实例进行分类，并利用投票程序为正射影像对象分配最终标签。所提出的 MV-OBIA 还与基于双向反射分布函数 (BRDF) 模拟的分类方法进行了比较。最后，为了减少 MV-OBIA 的基于多视图对象的数据生成的计算负担并使所提出​​的方法在实践中更具操作性，本研究提出了两种基于窗口的 MV-OBIA 实现，它们利用定位在几何图形上的窗口。对象实例的质心，而不是对象实例本身，以提取特征。第一个基于窗口的MV-OBIA采用固定窗口大小（记为FWMV-OBIA），而第二个基于窗口的MV-OBIA使用自适应窗口大小（记为AWMV-OBIA）。我们的结果表明，与 Ortho-OBIA 相比，MV-OBIA 显着提高了整体精度，无论我们研究地点用于湿地覆盖分类和类型的特征如何。此外，与基于 BRDF 的方法相比，MV-OBIA 在利用多视图信息进行分类方面也表现出更高的效率，因为它的总体准确度要高得多。最后，与 MV-OBIA 相比，FWMV-OBIA 和 AWMV-OBIA 都显示出在显着降低计算成本的情况下产生相同甚至更高的总体精度的潜力。