Practical Bottom-up Golf Course Detection Using Multispectral Remote Sensing Imagery

Jingbo Chen, Chengyi Wang, Dong-xu He, Jiansheng Chen, Anzhi Yue

Abstract: The rapid growth of golf course has constituted a nonnegligible threat to conservation of cropland and water resource in China. To monitor golf course at a large scale with low cost, a practical bottom-up golf course detection approach using multispectral remote sensing imagery is proposed. First of all, turfgrass, water-body and bunker are determined as the basic elements based on analyzing golf course land-use characteristics. Secondly, turfgrass and water-body are extracted using spectral indexes and these two basic elements are combined as region-of-interest under guidance of prior-knowledge. Afterwards, bunker is extracted by spectral mixture analysis restricted to region-of-interest. Finally, fuzzy C-means is adopted to recognize golf course using landscape metrics. A SPOT-5 HRG multispectral image of Beijing is used to validate the proposed method, and detection rate and false alarm rate are 86.67% and 38.10% respectively.