论文标题
天际线变化允许使用语义分割估算景观照片上的树木的距离
Skyline variations allow estimating distance to trees on landscape photos using semantic segmentation
论文作者
论文摘要
近似距离估计可用于确定基本景观特性，包括复杂性和开放性。我们表明，景观照片天际线的变化可用于估计地平线上的树木的距离。已经开发了基于天际线的变化的方法，并用于研究与天际线对象距离的潜在关系。由以像素表达的天际线高度定义的天际线信号被提取以进行多个土地使用/覆盖面积调查（LUCAS）景观照片。在语言数据集（可可）数据集中用常见对象训练的DeepLabv3+对照片进行了分割。这提供了形成天际线对象的像素级分类。还应用了条件随机场（CRF）算法以增加天际线信号的细节。然后考虑三个能够捕获天际线信号变化的指标进行分析。这些指标显示了与树木类别的距离的功能关系，树木的轮廓具有分形性质。特别是，针对基于475的ORTHO-PHOTO距离测量值进行了回归分析，在最好的情况下，R2评分达到了等于0.47。这是一个令人鼓舞的结果，它显示了天际线变化指标的潜力来推断距离相关信息。
Approximate distance estimation can be used to determine fundamental landscape properties including complexity and openness. We show that variations in the skyline of landscape photos can be used to estimate distances to trees on the horizon. A methodology based on the variations of the skyline has been developed and used to investigate potential relationships with the distance to skyline objects. The skyline signal, defined by the skyline height expressed in pixels, was extracted for several Land Use/Cover Area frame Survey (LUCAS) landscape photos. Photos were semantically segmented with DeepLabV3+ trained with the Common Objects in Context (COCO) dataset. This provided pixel-level classification of the objects forming the skyline. A Conditional Random Fields (CRF) algorithm was also applied to increase the details of the skyline signal. Three metrics, able to capture the skyline signal variations, were then considered for the analysis. These metrics shows a functional relationship with distance for the class of trees, whose contours have a fractal nature. In particular, regression analysis was performed against 475 ortho-photo based distance measurements, and, in the best case, a R2 score equal to 0.47 was achieved. This is an encouraging result which shows the potential of skyline variation metrics for inferring distance related information.
