Improvement of urban land use and land cover classification approach in arid areas

Jing Qian; Qiming Zhou; Xi Chen

doi:10.1117/12.864992

23 October 2010 Improvement of urban land use and land cover classification approach in arid areas

Jing Qian, Qiming Zhou, Xi Chen

Proceedings Volume 7830, Image and Signal Processing for Remote Sensing XVI; 78301H (2010) https://doi.org/10.1117/12.864992
Event: SPIE Remote Sensing, 2010, Toulouse, France

Abstract

Extraction of urban land-use information is base step of urban change detection. However, challenges remain in automatic delineation of urban areas and differentiation of finer inner-city land cover types. The extraction accuracy of built-up area is still unsatisfactory. This is mainly due to the heterogeneity nature of urban areas, where continuous and discrete elements occur side by side. Another reason is the mixed pixel problem, which is particularly serious in an urban environment. The built-up areas in arid areas may confuse with nearby bare soil and stony desert, which present very similar spectral characteristics as construction materials such as concrete, while they are often surrounded by farmland. This study focuses on improving urban land use and land cover classification approach in typical city of China's west arid areas using multi-sensor data. Pixel-based classification of the NDBI and Maximum Likelihood Classification (MLC) and object-oriented image classification were used in the study and the classification dataset including Landsat ETM (1999), CBERS (2005), and Beijing-1 (2006). The accuracy is assessed using high-resolution images, aerial photograph and field investigation data. The traditional pixel-based classification approach typically yield large uncertainty in the classification results. Object-oriented processing techniques are becoming more popular compared to traditional pixel-based image analysis.

Citation Download Citation

Jing Qian, Qiming Zhou, and Xi Chen "Improvement of urban land use and land cover classification approach in arid areas", Proc. SPIE 7830, Image and Signal Processing for Remote Sensing XVI, 78301H (23 October 2010); https://doi.org/10.1117/12.864992

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