4 November 2005 A combined segmentation and pixel-based classification approach of QuickBird imagery for land cover mapping
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Proceedings Volume 6044, MIPPR 2005: Image Analysis Techniques; 60440U (2005); doi: 10.1117/12.654785
Event: MIPPR 2005 SAR and Multispectral Image Processing, 2005, Wuhan, China
Recent advances in remote-sensing technology suggest that satellite-based earth observation (EO) has great potential for providing and updating spatial information in a timely and cost-effective manner. However, with the improvement of the spatial resolution of satellite image, the detail of the image has become more complicated. Even though texture features included for multi-spectral high-resolution satellite imagery, conventional methods for pixel-based classification have limited success. In order to take better advantage of spatial information of high-resolution satellite imagery, a combined segmentation and pixel-based classification approach is presented in this paper. Firstly, pixel-based multi-spectral maximum-likelihood classification approach obtains initial classification result. Secondly, image segmentation is created by watershed transform and region merging. Finally, based on the proportions of each class present in each segment obtain final classification map. A QuickBird imagery of the suburban area of Shanghai in China is used to validate the proposed method. Experiment proves that classification map produced by the combined approach, is visual noise-free, has clean borders, and has better classification accuracy than that by pixel-based classification approach.
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Jianmei Wang, Deren Li, Wenzhong Qin, "A combined segmentation and pixel-based classification approach of QuickBird imagery for land cover mapping", Proc. SPIE 6044, MIPPR 2005: Image Analysis Techniques, 60440U (4 November 2005); doi: 10.1117/12.654785; https://doi.org/10.1117/12.654785

Image segmentation

Image classification


Earth observing sensors

Satellite imaging

Remote sensing


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