| Type |
Article |
| Date |
2015-01 |
| Language |
English |
| Author(s) |
Rousseeuw Kevin1, 2, Poison Caillault Emilie1, Lefebvre Alain 2, Hamad Denis1 |
| Affiliation(s) |
1 : ULCO/LISIC, BP 719, FR-62228 Calais, France
2 : IFREMER, Centre Manche Mer du Nord, BP 699, FR-62321 Boulogne-sur-Mer, France |
| Source |
Ieee Journal Of Selected Topics In Applied Earth Observations And Remote Sensing (1939-1404) (Institute of Electrical and Electronics Engineers (IEEE)), 2015-01 , Vol. 8 , N. 1 , P. 204-213 |
| DOI |
10.1109/JSTARS.2014.2341219 |
| WOS© Times Cited |
16 |
| Keyword(s) |
Hybrid Hidden Markov Model, marine water monitoring, Phytoplankton blooms, spectral clustering |
| Abstract |
Phytoplankton is an important indicator of water quality assessment. To understand phytoplankton dynamics, many fixed buoys and ferry boxes were implemented, resulting in the generation of substantial data signals. Collected data are used as inputs of an effective monitoring system. The system, based on unsupervised hidden Markov model (HMM), is designed not only to detect phytoplancton blooms but also to understand their dynamics. HMM parameters are usually estimated by an iterative expectation-maximization (EM) approach. We propose to estimate HMM parameters by using spectral clustering algorithm. The monitoring system is assessed based on database signals from MAREL-Carnot station, Boulogne-sur-Mer, France. Experimental results show that the proposed system is efficient to detect environmental states such as phytoplankton productive and nonproductive periods without a priori knowledge. Furthermore, discovered states are consistent with biological interpretation. |
| Full Text |
| File |
Pages |
Size |
Access |
| Author's final draft |
13 |
580 KB |
Open access |
|
