Urban landscape classification using Chinese advanced high-resolution satellite imagery and an object-oriented multi-variable model

基于国产高分辨率遥感影像和面向对象多变量模型的城市土地利用分类（英文）

The Chinese ZY-1 02C satellite is one of the most advanced high-resolution earth observation systems designed for terrestrial resource monitoring. Its capability for comprehensive landscape classification, especially in urban areas, has been under constant study. In view of the limited spectral resolution of the ZY-1 02C satellite （three bands）, and the complexity and hetero- geneity across urban environments, we attempt to test its performance of urban landscape classification by combining a multi- variable model with an object-oriented approach. The multiple variables including spectral reflection, texture, spatial autocorre- lation, impervious surface fraction, vegetation, and geometry indexes were first calculated and selected using forward stepwise linear discriminant analysis and applied in the following object-oriented classification process. Comprehensive accuracy as- sessment which adopts traditional error matrices with stratified random samples and polygon area consistency （PAC） indexes was then conducted to examine the real area agreement between a classified polygon and its references. Results indicated an overall classification accuracy of 92.63% and a kappa statistic of 0.9124. Furthermore, the proposed PAC index showed that more than 82% of all polygons were correctly classified. Misclassification occurred mostly between residential area and barren/farmland. The presented method and the Chinese ZY-1 02C satellite imagery are robust and effective for urban landscape classification.

目的:资源一号02C星搭载国产遥感卫星序列中为数不多的高性能传感器之一,获取大量的影像数据。然而,在空间分辨率相对较高,光谱分辨率比较低的情况下,城市土地覆盖分类势必存在一定问题。如何深度挖掘影像光谱和空间信息,建立可行的技术方法流程,实现准确的城市土地覆盖分类,进而为其推广应用奠定基础十分必要。创新点:提出光谱与空间领域信息、判别分析、面向对象法结合的技术流程体系(图2),实现城市土地覆盖的准确分类。对分类结果采用基于点和图斑面积的两种验证方法进行验证。方法:计算图像纹理、空间自相关特征、形状指数、植被指数、不透水面含量等信息,与光谱信息结合,经过判别分析和相关分析的筛选,实现面向对象的分类和两种指标的精度评价。结论:根据本文提出的技术路线,可以实现相对准确的城市土地覆盖分类。总体点位精度在92%以上(表2),面积精度达到82%以上,误差通常源自住宅和裸土的混淆。影像数据在城市土地覆盖分类方面非常有效。