Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery

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• 作者：Soe W. Myint^a ; ^{soe.myint@asu.edu} ; Patricia Gober^a ; ^{gober@asu.edu} ; Anthony Brazel^a ; ^{abrazel@asu.edu} ; Susanne Grossman-Clarke^b ; ^d ; ^{sg.clarke@asu.edu} ; Qihao Weng^c ; ^{qweng@indstate.edu}
• 关键词：Urban ; High resolution ; Object-based classifier ; Membership function ; Nearest neighbor
• 刊名：Remote Sensing of Environment
• 出版年：2011
• 出版时间：15 May 2011
• 年：2011
• 卷：115
• 期：5
• 页码：1145-1161
• 全文大小：3384 K

文摘

In using traditional digital classification algorithms, a researcher typically encounters serious issues in identifying urban land cover classes employing high resolution data. A normal approach is to use spectral information alone and ignore spatial information and a group of pixels that need to be considered together as an object. We used QuickBird image data over a central region in the city of Phoenix, Arizona to examine if an object-based classifier can accurately identify urban classes. To demonstrate if spectral information alone is practical in urban classification, we used spectra of the selected classes from randomly selected points to examine if they can be effectively discriminated. The overall accuracy based on spectral information alone reached only about 63.33 % . We employed five different classification procedures with the object-based paradigm that separates spatially and spectrally similar pixels at different scales. The classifiers to assign land covers to segmented objects used in the study include membership functions and the nearest neighbor classifier. The object-based classifier achieved a high overall accuracy (90.40 % ), whereas the most commonly used decision rule, namely maximum likelihood classifier, produced a lower overall accuracy (67.60 % ). This study demonstrates that the object-based classifier is a significantly better approach than the classical per-pixel classifiers. Further, this study reviews application of different parameters for segmentation and classification, combined use of composite and original bands, selection of different scale levels, and choice of classifiers. Strengths and weaknesses of the object-based prototype are presented and we provide suggestions to avoid or minimize uncertainties and limitations associated with the approach.