TY - JOUR
AU - Tian, Jinwen
AB - 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.
TI - Hyperspectral image segmentation using spectral-spatial constrained conditional random field
JO - Proceedings of SPIE
DO - 10.1117/12.901789
DA - 2011-11-04
UR - https://www.deepdyve.com/lp/spie/hyperspectral-image-segmentation-using-spectral-spatial-constrained-4gxRDvaxjs
SP - 800213
EP - 800213-8
VL - 8002
IS - 1
DP - DeepDyve
ER -