Superpixel-based Markov random field for classification of hyperspectral images
Superpixel-based Markov random field for classification of hyperspectral images
复制标题
DOI：
10.1109/igarss.2013.6723581
复制
发表时间：
2013-07
期刊：
影响因子：
--
通讯作者：
Shanshan Li;X. Jia;Bing Zhang
中科院分区：
文献类型：
--
作者：
Shanshan Li;X. Jia;Bing Zhang
提出了一种基于超像素和马尔可夫随机场的监督分类方法。超像素是马尔可夫随机场的基本单位，而不是在像素级操作的高光谱图像过分割。引入自适应权系数来确定超像素之间的上下文关系。实现支持向量机是为了更好地估计光谱对这种方法的贡献。对真实的高光谱图像的实验表明，该算法具有较好的性能.
The paper presents a supervised classification method based on superpixels and Markov random field (MRF). Hyperspectral image is over-segmented into superpixels that are as basic unit of Markov random field instead of operating at the pixel level. Adaptive weight coefficient is introduced to determine contextual relationship between superpixels. Support vector machines are implemented for better estimation of spectral contribution to this approach. An experiment of real hyperspectral image reveals efficient performance.