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Explicit Area-Based Accuracy Assessment for Mangrove Tree Crown Delineation using Geographic Object-Based Image Analysis (GEOBIA)

机译:使用基于地理对象的图像分析(Geobia)的红树林树冠描绘的明确区域准确性评估

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Effective mangrove management requires spatially explicit information of mangrove tree crown map as a basis for ecosystem diversity study and health assessment. Accuracy assessment is an integral part of any mapping activities to measure the effectiveness of the classification approach. In geographic object-based image analysis (GEOBIA) the assessment of the geometric accuracy (shape, symmetry and location) of the created image objects from image segmentation is required. In this study we used an explicit area-based accuracy assessment to measure the degree of similarity between the results of the classification and reference data from different aspects, including overall quality (OQ), user's accuracy (UA), producer's accuracy (PA) and overall accuracy (OA). We developed a rule set to delineate the mangrove tree crown using WorldView-2 pan-sharpened image. The reference map was obtained by visual delineation of the mangrove tree crowns boundaries form a very high-spatial resolution aerial photograph (7.5cm pixel size). Ten random points with a 10 m radius circular buffer were created to calculate the area-based accuracy assessment. The resulting circular polygons were used to clip both the classified image objects and reference map for area comparisons. In this case, the area-based accuracy assessment resulted 64% and 68% for the OQ and OA, respectively. The overall quality of the calculation results shows the class-related area accuracy; which is the area of correctly classified as tree crowns was 64% out of the total area of tree crowns. On the other hand, the overall accuracy of 68% was calculated as the percentage of all correctly classified classes (tree crowns and canopy gaps) in comparison to the total class area (an entire image). Overall, the area-based accuracy assessment was simple to implement and easy to interpret. It also shows explicitly the omission and commission error variations of object boundary delineation with colour coded polygons.
机译:有效的红树林管理需要海革树冠地图的空间明确信息作为生态系统多样性研究和健康评估的基础。准确性评估是衡量分类方法的有效性的任何映射活动的组成部分。在基于地理对象的图像分析(Geobia)中,需要从图像分割的创建图像对象的几何精度(形状,对称性和位置)评估。在这项研究中,我们使用了明确的基于区域的准确性评估,以测量来自不同方面的分类和参考数据的结果之间的相似性,包括整体质量(OQ),用户的准确性(UA),生产者的准确性(PA)和整体准确性(OA)。我们开发了一个规则,以使用WorldView-2泛尖的图像描绘红树林树冠。参考图是通过对红树林树冠边界的视觉描绘而获得的,形成非常高空间分辨率的航拍照片(7.5cm像素尺寸)。创建具有10 M个半径循环缓冲器的十个随机点,以计算基于区域的精度评估。由此产生的圆形多边形用于剪辑分类的图像对象和参考图以进行面积比较。在这种情况下,区分的基于区域的精度评估分别为OQ和OA产生了64%和68%。计算结果的整体质量显示了类相关的区域精度;这是树冠被正确归类的地区为树冠的总面积为64%。另一方面,与总类别(整个图像)相比,将68%的总精确度计算为所有正确分类的类(树冠和天窗差距)的百分比。总体而言,基于地区的准确性评估易于实施,易于解释。它还明确地显示了与彩色编码多边形对象边界描绘的遗漏和佣金误差变化。

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