利用DMSP/OLS灯光影像提取城镇建成区的聚类阈值法

A Clustered Threshold Method for Extracting Urban Built-up Area Using the DMSP/OLS Nighttime Light Images

提出了便捷、高效地提取城镇建成区的聚类阈值法。该方法以连通区域循环标识算法所识别的建成区对象为基本空间单元,以对象的规模和地理重心处像元灰度(DN)值的大小为不同城镇化发展等级的衡量指标,进行空间聚类;然后,借助统计数据确定各聚类区域的最佳灯光阈值序列,提取城镇建成区,并对各建成区对象进行几何形态优化,利用消除运算去除细小碎片,填充运算填充内部空洞,平滑运算消除边缘锯齿。将提取结果与历年的《中国统计年鉴》数据和利用Google Earth提取的影像数据进行对比分析,结果表明,聚类阈值法能较好地提取城镇建成区的面积总量信息和空间格局特征,在数量尺度和空间格局上均有较高的有效性及可靠性。

A clustered threshold method for conveniently and efficiently extracting urban built-up areas has been proposed in this study.This method has broken through the limitations created by single pixel analysis and the administrative boundaries by using built-up objects identified by the recursive connected-region labeling algorithm as basic spatial units.It classifies urban development levels using spatial clustering on the basis of the size of the objects and the DN value of the geographic center of objects and extracts built-up areas based on the optimal threshold sequence as determined from statistical data.It optimizes the geometric morphology of built-up objects by removing small scraps,stuffing internal holes,and smoothing the jagged edges.Extraction results were analyzed and compared to the statistical data found in the China Statistical Yearbook over the years and images derived from Google Earth.These results show that the clustered threshold method can effectively obtain the total acreage and spatial pattern of urban built-up areas,with high validity and reliability in both quantity scale and spatial pattern.