TY - GEN
T1 - Target recognition based on rough set and data fusion in remote sensing image
AU - Wang, Jianhong
AU - Li, Xin
AU - Tao, Tangfei
AU - Han, Chongzhao
PY - 2006
Y1 - 2006
N2 - Focused on uncertainty of recognition of sensitive / interesting targets in the remote sensing image, a new scheme based on Rough set theory is employed. Firstly, a summary of data resource, the features of recognition, and the process of the traditional target recognition Is given. Then, we Introduce the theory of Rough Set briefly. Thirdly, the original features selection, features reduction and weighted set of the features calculating based on RS, and the strategy of recognition based on the decision-making are proposed in detail. Finally, the steps of the scheme and some examples are presented respectively. As a result, 14 features can be reduced to 10, and the recognition rate nearly reaches 100%, which is wonderful. It is shown that the scheme not only ensures the high recognition rate, reduces the dimension of feature vector, decreases the storage space of data and improves the efficiency of calculation, but also is be propitious to build ATR knowledge base and update the data of the database as well.
AB - Focused on uncertainty of recognition of sensitive / interesting targets in the remote sensing image, a new scheme based on Rough set theory is employed. Firstly, a summary of data resource, the features of recognition, and the process of the traditional target recognition Is given. Then, we Introduce the theory of Rough Set briefly. Thirdly, the original features selection, features reduction and weighted set of the features calculating based on RS, and the strategy of recognition based on the decision-making are proposed in detail. Finally, the steps of the scheme and some examples are presented respectively. As a result, 14 features can be reduced to 10, and the recognition rate nearly reaches 100%, which is wonderful. It is shown that the scheme not only ensures the high recognition rate, reduces the dimension of feature vector, decreases the storage space of data and improves the efficiency of calculation, but also is be propitious to build ATR knowledge base and update the data of the database as well.
KW - Automatic/ aided target recognition (ATR)
KW - Image understanding (IU)
KW - Information fusion
KW - Remote sensing
KW - Rough set
UR - https://www.scopus.com/pages/publications/34047206160
U2 - 10.1109/WCICA.2006.1714045
DO - 10.1109/WCICA.2006.1714045
M3 - 会议稿件
AN - SCOPUS:34047206160
SN - 1424403324
SN - 9781424403325
T3 - Proceedings of the World Congress on Intelligent Control and Automation (WCICA)
SP - 10420
EP - 10424
BT - Proceedings of the World Congress on Intelligent Control and Automation (WCICA)
T2 - 6th World Congress on Intelligent Control and Automation, WCICA 2006
Y2 - 21 June 2006 through 23 June 2006
ER -