DocumentCode
554006
Title
Notice of Retraction
Clustering analysis on disease severity of wheat stripe rust based on SOM neural network
Author
Yang Ke-ming ; Xue Zhao-hui ; Li Hong-Wei ; Cui Li ; Ran Ying-ying ; Zhang Yong-jie
Author_Institution
Dept. of Remote Sensing, China Univ. of Min. & Technol. (Beijing), Beijing, China
Volume
1
fYear
2011
fDate
26-28 July 2011
Firstpage
421
Lastpage
425
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
A SOM (Self-organizing Feature Maps) model was introduced to cluster and analysis on the disease severity of wheat stripe rust based on PHI (Pushbroom hyperspectral imager) data. By means of acquiring the spectral index data (SID) and spectral angle data (SAD) of the samples, combining with the samples´ spectral average reflectance data (ARD), three two-dimensional data matrixes were obtained as the inputs of SOM model. After iterative learning and self-organized clustering, the models´ outputs farthest approached to the reality in 3-dimensional severity space of wheat stripe rust. Then, with the net-trained, all data of the trial plot were simulated. The simulating results demonstrate that the division of wheat stripe rust severity is obviously. The whole trial spot was derived into four grades and the results are satisfactory.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
A SOM (Self-organizing Feature Maps) model was introduced to cluster and analysis on the disease severity of wheat stripe rust based on PHI (Pushbroom hyperspectral imager) data. By means of acquiring the spectral index data (SID) and spectral angle data (SAD) of the samples, combining with the samples´ spectral average reflectance data (ARD), three two-dimensional data matrixes were obtained as the inputs of SOM model. After iterative learning and self-organized clustering, the models´ outputs farthest approached to the reality in 3-dimensional severity space of wheat stripe rust. Then, with the net-trained, all data of the trial plot were simulated. The simulating results demonstrate that the division of wheat stripe rust severity is obviously. The whole trial spot was derived into four grades and the results are satisfactory.
Keywords
crops; geophysical image processing; matrix algebra; pattern clustering; self-organising feature maps; SOM neural network; clustering analysis; disease severity; pushbroom hyperspectral imager data; self organizing feature maps; spectral angle data; spectral average reflectance data; spectral index data; two dimensional data matrixes; wheat stripe rust; Agriculture; Diseases; Neurons; Reflectivity; Rendering (computer graphics); Simulation; Training; Clustering analysis; Disease severity; PHI image; SOM neural network; Wheat stripe rust;
fLanguage
English
Publisher
ieee
Conference_Titel
Natural Computation (ICNC), 2011 Seventh International Conference on
Conference_Location
Shanghai
ISSN
2157-9555
Print_ISBN
978-1-4244-9950-2
Type
conf
DOI
10.1109/ICNC.2011.6022114
Filename
6022114
Link To Document