Abstract Accurate and timely monitoring of urban land-use and land-cover (LULC) change is useful for understanding the various impacts of human activity on the urban environment. In order to demonstrate the advantage of time series imaging for urban LULC change detection, we selected time series Landsat images over a two-year period to detect inter-annual changes. A time series trajectory for each pixel was developed by the biophysical composition index (BCI) and normalized vegetation index (NDVI) values extracted from Landsat images. Considering that temporal length of trajectories in different years might be different, the dynamic time warping (DTW) measure was selected as the LULC change magnitude indicator. After DTW-based change/unchanged detection, the DTW-based clustering method was used in LULC change type extraction. Finally, the overall accuracy of change/unchanged detection result was 92.3%, and the overall accuracy of all change types was 71%. Some change types that are difficult to extract by bi-temporal images were detected, such as inter-class changes between farmland and forest, and intra-class change of farmland, indicating the advantage of time series information in LULC change detection.

中文翻译：

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使用基于动态时间规整的时间序列聚类方法的土地利用和土地覆盖变化检测

摘要 准确及时地监测城市土地利用和土地覆盖（LULC）变化有助于了解人类活动对城市环境的各种影响。为了展示时间序列成像在城市 LULC 变化检测中的优势，我们选择了两年的时间序列 Landsat 图像来检测年际变化。每个像素的时间序列轨迹由从 Landsat 图像中提取的生物物理成分指数 (BCI) 和归一化植被指数 (NDVI) 值开发。考虑到不同年份轨迹的时间长度可能不同，选择动态时间扭曲（DTW）度量作为LULC变化幅度指标。在基于DTW的变化/不变检测之后，基于DTW的聚类方法用于LULC变化类型提取。最后，变化/不变检测结果总体准确率为92.3%，所有变化类型总体准确率为71%。检测到一些双时相图像难以提取的变化类型，如农田和森林的类间变化、农田的类内变化，表明时间序列信息在LULC变化检测中的优势。