Spectral-Spatial Based Super Pixel Remote Sensing Image Classification
Apart from the rich spectral information provided by multispectral or hypersepctral sensors, the spatial information has been paid more and more attention in remote sensing classification, especially for high spatial resolution images. Pixel-wise spatial features can be generated by applying Gray Level Cooccurrence Matrix (GLCM) locally to describe an images texture properties. Morphological filtering provides spatial structure enhancement and watershed processing aims at contextual boundary identification. In this paper, the advantages and disadvantages of these spatial treatments are investigated. A combined procedure is developed to maximize spatial information extraction. Texture feature selection is emphasized for class separability enhancement. Morphological filtering is introduced as a preprocessing for watershed segmentation in order to reduce false alarm on contextual boundaries. Super pixels are formed for the objects defined from the watershed segmentation. The experimental results show that the combined spatial treatment is effective and, by integrating it with spectral information, an object oriented classification map can be obtained with significantly reduced salt and pepper noise.
remote sensing texture morphological filtering watershed super pixel
Guangyun Zhang Xiuping Jia Ngai M. Kwok
School of Engineering and Information Technology, University College, The University of New South Wa School of Mechanical and Manufacturing Engineering, The University of New South Wales, Sydney, Austr
国际会议
2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)
上海
英文
1709-1713
2011-10-15（万方平台首次上网日期，不代表论文的发表时间）