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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)对红树林树冠轮廓进行基于区域的显式精度评估

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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:有效的红树林管理要求在空间上明确显示红树林树冠图信息,以此作为生态系统多样性研究和健康评估的基础。准确性评估是任何测绘活动中不可或缺的部分,以衡量分类方法的有效性。在基于地理对象的图像分析(GEOBIA)中,需要从图像分割中评估所创建图像对象的几何精度(形状,对称性和位置)。在这项研究中,我们使用了基于区域的显式准确性评估,以从不同方面衡量分类结果与参考数据之间的相似度,这些方面包括总体质量(OQ),用户准确性(UA),生产商准确性(PA)和总体精度(OA)。我们开发了一个规则集,用于使用WorldView-2全景图像描绘红树林树冠。参考图是通过对红树林树冠边界进行视觉描绘而形成的,该边界形成了非常高空间分辨率的航拍照片(7.5厘米像素大小)。创建10个半径为10 m的圆形缓冲区的随机点,以计算基于面积的精度评估。生成的圆形多边形用于修剪分类的图像对象和参考图,以进行区域比较。在这种情况下,基于区域的准确性评估得出OQ和OA分别为64%和68%。计算结果的整体质量显示与类相关的区域准确性;这是正确分类为树冠的面积,占树冠总面积的64%。另一方面,将所有正确分类的类别(树冠和树冠间隙)相对于总类别区域(整个图像)的百分比计算为68%的总体准确性。总体而言,基于区域的准确性评估易于实施且易于解释。它还清楚地显示了带有颜色编码多边形的对象边界轮廓的遗漏和委托误差变化。

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