Spatial Knowledge Based Complicated Urban Area Classification from High-Resolution Remote Sensing Image
Combining spectral and spatial information can improve land use classification of high-resolution data. However, the use of spatial information always focus on objects’ spatial pattern, whereas not pay enough attention to spatial relationship, which is more convenient and effective in remote sensing classification. This letter proposes a spectralspatial information method, which aims to exploit objects’ spatial relationships in high resolution imagery, and then integrate it with spectral information in remote sensing classification. We experiment on urban mapping based on spectral-spatial information using Quickbird imagery, and compare its result with supervised classification methods like maximum likelihood classification, and support vector machine (SVM) classification. The results show that the proposed method yield better performance than the others in both precision and rationality.
Spatial knowledge urban area classification remote sensing image processing
Cheng Qiao Jiancheng Luo Zhanfeng Shen Zhiwen Zhu Wei Wu
Institute of Remote Sensing Applications,CAS No.20 Datun Rd. Chaoyang District P.O.Box9718 Beijing,100101,P.R China
国际会议
福州
英文
405-408
2011-06-29（万方平台首次上网日期，不代表论文的发表时间）