Paper
5 December 2011 An improved membrane algorithm for solving time-consuming water quality retrieval
Author Affiliations +
Proceedings Volume 8005, MIPPR 2011: Parallel Processing of Images and Optimization and Medical Imaging Processing; 800509 (2011) https://doi.org/10.1117/12.901923
Event: Seventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2011), 2011, Guilin, China
Abstract
Retrieving the parameters in water quality with multispectral data using neural network is increasingly popular, however, the training process with large amount samples and calculation with large-volume data are a time-consuming work. Many emergency pollution events need quick responses for practical use. In this paper, an improved membrane computing strategy is presented. This strategy is a hybrid one combining the framework and evolution rules of P systems with active membranes and neural networks, and it involves a dynamic structure including membrane fusion and division, which helpful to enhance the information communication and beneficial to reduce the computation. Then, a parallel implementation with the training result is discussed. Experiments with Landsat datasets to obtain suspended sediment are carried out to demonstrate the practical capabilities of this introduced strategy.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Liang Zhong and Wenfei Luo "An improved membrane algorithm for solving time-consuming water quality retrieval", Proc. SPIE 8005, MIPPR 2011: Parallel Processing of Images and Optimization and Medical Imaging Processing, 800509 (5 December 2011); https://doi.org/10.1117/12.901923
Lens.org Logo
CITATIONS
Cited by 1 scholarly publication.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Neural networks

Evolutionary algorithms

Neurons

Skin

Computing systems

Telecommunications

Earth observing sensors

Back to Top