Hyperspectral Image Segmentation Using Spectral-Spatial Constrained Conditional Random Field

摘要：

In this paper, we propose a hyperspectral image segmentation algorithm which combines classification and segmentation into Conditional Random Field(CRF) framework. The classification step is implemented using Gaussian process which gives the exact class probabilities of a pixel. The classification result is treated as the single-pixel model in CRF framework, by which classification and segmentation are combined together. Through the CRF, the spatial and spectral constraints on pixel classification are exploited. As a result, experimental results on real hyperspectral image show that the segmentation precision has been much improved.

关键词： hyperspectral image classification segmentation Gaussian process conditional random field

作者: Airong Sun Yihua Tan Jinwen Tian

作者单位: State Key Laboratory for Multi-spectral Information Processing Technology Huazhong University of Sci State Key Laboratory for Multi-spectral Information Processing TechnologyHuazhong University of Scie

会议类型: 国际会议

会议名称: 第七届多光谱图象处理与模式识别国际学术会议

会议地点: 桂林

会议语种:英文

页码: 1-8

在线出版日期: 2011-11-01（万方平台首次上网日期，不代表论文的发表时间）