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 spectral-spatial 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.