TY - GEN
T1 - A hierarchical superpixel aggregation model for hyperspectral image
AU - Han, Bingnan
AU - Yin, Jihao
AU - Luo, Xiaoyan
AU - Qv, Hui
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/12/1
Y1 - 2017/12/1
N2 - Superpixel has been widely applied in hyperspectral image processing as a pre-processing step for over-segmentation. However, most superpixel algorithms are difficult to control the segmentation balance between fragmentation and accuracy. In this paper, we propose a superpixel aggregation model to cluster the over-segmentations. Based on the own importance and interrelationship of superpixels, a two-step merging procedure is designed in the hierarchical wise from local to global comparisons. Aiding by a density peak metric, which is to exploit the spectral correlation in hyperspectral image, the similar neighbor superpixels are merged firstly, and then the similar regions in discontinuous spatial location are gathered. Experimental results show that the proposed model can achieve high accuracy in low region number compared with original superpixel algorithm, and the performance for unsupervised classification application is also remarkable.
AB - Superpixel has been widely applied in hyperspectral image processing as a pre-processing step for over-segmentation. However, most superpixel algorithms are difficult to control the segmentation balance between fragmentation and accuracy. In this paper, we propose a superpixel aggregation model to cluster the over-segmentations. Based on the own importance and interrelationship of superpixels, a two-step merging procedure is designed in the hierarchical wise from local to global comparisons. Aiding by a density peak metric, which is to exploit the spectral correlation in hyperspectral image, the similar neighbor superpixels are merged firstly, and then the similar regions in discontinuous spatial location are gathered. Experimental results show that the proposed model can achieve high accuracy in low region number compared with original superpixel algorithm, and the performance for unsupervised classification application is also remarkable.
KW - Clustering
KW - Hierarchical segmentation
KW - Hyperspectral
KW - Superpixel
KW - Unsupervised classification
UR - https://www.scopus.com/pages/publications/85041802535
U2 - 10.1109/IGARSS.2017.8127819
DO - 10.1109/IGARSS.2017.8127819
M3 - 会议稿件
AN - SCOPUS:85041802535
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 3767
EP - 3770
BT - 2017 IEEE International Geoscience and Remote Sensing Symposium
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017
Y2 - 23 July 2017 through 28 July 2017
ER -