A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf
.
Application of maximum likelihood classification Based on minimal risk in crop interpretation
2015
Proceedings of the 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering 2015
unpublished
In crop interpretation by remote sensing, Gray distribution of crop is overlapped in some intervals. The non-target crops fall into the target crop, which would greatly increase the workload in post classification. To reduce these classification errors, and improve accuracy of clarification, maximum likelihood classification based on minimal risk is used. And the relationship between extraction rate and accuracy were analyzed. Experiments show that this method can improve the accuracy of
doi:10.2991/icmmcce-15.2015.516
fatcat:lyrcjhdfhnfbtmdxeszxeijrxy