Department of City and Regional Planning, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
Department of Computer and Information Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
PLoS One. 2020 May 7;15(5):e0230856. doi: 10.1371/journal.pone.0230856. eCollection 2020.
To analyze types and patterns of greening trends across a city, this study seeks to identify a method of creating very high-resolution urban vegetation maps that scales over space and time. Vegetation poses unique challenges for image segmentation because it is patchy, has ragged boundaries, and high in-class heterogeneity. Existing and emerging public datasets with the spatial resolution necessary to identify granular urban vegetation lack a depth of affordable and accessible labeled training data, making unsupervised segmentation desirable. This study evaluates three unsupervised methods of segmenting urban vegetation: clustering with k-means using k-means++ seeding; clustering with a Gaussian Mixture Model (GMM); and an unsupervised, backpropagating convolutional neural network (CNN) with simple iterative linear clustering superpixels. When benchmarked against internal validity metrics and hand-coded data, k-means is more accurate than GMM and CNN in segmenting urban vegetation. K-means is not able to differentiate between water and shadows, however, and when this segment is important GMM is best for probabilistically identifying secondary land cover class membership. Though we find the unsupervised CNN shows high degrees of accuracy on built urban landscape features, its accuracy when segmenting vegetation does not justify its complexity. Despite limitations, for segmenting urban vegetation, k-means has the highest performance, is the simplest, and is more efficient than alternatives.
为分析城市中绿化趋势的类型和模式，本研究旨在寻找一种能够跨越空间和时间尺度创建超高分辨率城市植被图的方法。植被给图像分割带来了独特的挑战，因为它具有斑块状、边界参差不齐和高类内异质性。现有的和新兴的公共数据集具有识别细粒度城市植被所需的空间分辨率，但缺乏可负担和可获得的标记训练数据，因此非监督分割是可取的。本研究评估了三种用于分割城市植被的非监督方法：使用 K-means++ 种子的 k-均值聚类；高斯混合模型 (GMM) 聚类；以及具有简单迭代线性聚类超像素的无监督反向传播卷积神经网络 (CNN)。当与内部有效性指标和手工编码数据进行基准测试时，k-均值在分割城市植被方面比 GMM 和 CNN 更准确。然而，k-均值无法区分水和阴影，当这个区域很重要时，GMM 最适合概率识别次要土地覆盖类别的成员资格。尽管存在限制，但对于分割城市植被，k-均值的性能最高，最简单，比其他方法更高效。
