Improving boundary classification accuracy of remotely sensed images using weighted semivariogram

Shoujing Yin; Xiaoling Chen

doi:10.1117/12.749719

14 November 2007 Improving boundary classification accuracy of remotely sensed images using weighted semivariogram

Shoujing Yin, Xiaoling Chen

Author Affiliations +

Proceedings Volume 6789, MIPPR 2007: Medical Imaging, Parallel Processing of Images, and Optimization Techniques; 67892S (2007) https://doi.org/10.1117/12.749719
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

Abstract

The spectral dependence between neighboring pixels in the remotely sensed image is useful for the discrimination of land use/cover types, but it is neglected in most classical classification algorithms. Variogram-based method is popular in exploiting the spatial information of spectrum in the remotely sensed images. Although this method has been utilized in many ways, it still tends to misclassify the pixels near boundaries in practice and therefore leads to boundary-blurring problem. A weighted semivariogram (WSV) method is proposed to solve that problem in this paper, and the results also show good performance in improving the boundary classification accuracy of remotely sensed images.

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Shoujing Yin and Xiaoling Chen "Improving boundary classification accuracy of remotely sensed images using weighted semivariogram", Proc. SPIE 6789, MIPPR 2007: Medical Imaging, Parallel Processing of Images, and Optimization Techniques, 67892S (14 November 2007); https://doi.org/10.1117/12.749719

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KEYWORDS

Image classification

Earth observing sensors

Landsat

Algorithm development

Image processing

Image resolution

Remote sensing

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