An Efficient Unsupervised Index Based Approach for Mapping Urban Vegetation from IKONOS Imagery
Document Type
Article
Publication Date
8-2016
Keywords
Unsupervised classification, Vegetation index, Jenks natural breaks, Hierarchical clustering, Support vector machine, IKONOS imagery
Digital Object Identifier (DOI)
https://doi.org/10.1016/j.jag.2016.04.001
Abstract
Despite the increased availability of high resolution satellite image data, their operational use for mapping urban land cover in Sub-Saharan Africa continues to be limited by lack of computational resources and technical expertise. As such, there is need for simple and efficient image classification techniques. Using Bamenda in North West Cameroon as a test case, we investigated two completely unsupervised pixel based approaches to extract tree/shrub (TS) and ground vegetation (GV) cover from an IKONOS derived soil adjusted vegetation index. These included: (1) a simple Jenks Natural Breaks classification and (2) a two-step technique that combined the Jenks algorithm with agglomerative hierarchical clustering. Both techniques were compared with each other and with a non-linear support vector machine (SVM) for classification performance. While overall classification accuracy was generally high for all techniques (>90%), One-Way Analysis of Variance tests revealed the two step technique to outperform the simple Jenks classification in terms of predicting the GV class. It also outperformed the SVM in predicting the TS class. We conclude that the unsupervised methods are technically as good and practically superior for efficient urban vegetation mapping in budget and technically constrained regions such as Sub-Saharan Africa.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
International Journal of Applied Earth Observation and Geoinformation, v. 50, p. 211-220
Scholar Commons Citation
Anchang, Julius Y.; Ananga, Erick O.; and Pu, Ruiliang, "An Efficient Unsupervised Index Based Approach for Mapping Urban Vegetation from IKONOS Imagery" (2016). School of Geosciences Faculty and Staff Publications. 1351.
https://digitalcommons.usf.edu/geo_facpub/1351
