Type: Scientific article
Title: Object-based urban vegetation mapping with high-resolution aerial photography as a single data source
Author: Li, Xiaoxiao, Shao, Guofan
Journal: International Journal of Remote Sensing
Abstract: In this article, we demonstrate an object-oriented method for detailed urban vegeta- tion delineation by using 1 m resolution, four-band digital aerial photography as the only input data. A hierarchical classification scheme was developed to discriminate vegetation types at both coarse and fine scales. The processes of vegetation extrac- tion include the examination of spectral and spatial relationships, object geometry, and the hierarchical relationship of image objects. The advantages of four different seg- mentation methods were combined to identify feature similarities, both among image objects and with their neighbours. Image growth took place if those neighbours satis- fied a series of criteria given a set of features of class-defined objects. Object-based classification results demonstrated higher accuracy than those using pixel-based classi- fication methods. The object-oriented method achieved overall classification accuracies of 87.5%, 90.5%, and 90.5% at three different levels of class hierarchy, and very high producer’s accuracies were demonstrated in the classes of tree, crop, and different types of grass. The object-oriented classification method described here proved effective for separating vegetation types defined by life form, area, or shape without using additional remote-sensing data sources with different resolutions or any ancillary data such as digital elevation models.
Citation: Li, X., & Shao, G. (2013). Object-based urban vegetation mapping with high-resolution aerial photography as a single data source. International Journal of Remote Sensing, 34(3), 771–789. doi:10.1080/01431161.2012.714508