Accurate automatic quantification of taxa-specific plankton abundance using dual classification with correction
MetadataShow full item record
Optical imaging samplers are becoming widely used in plankton ecology, but image analysis methods have lagged behind image acquisition rates. Automated methods for analysis and recognition of plankton images have been developed, which are capable of real-time processing of incoming image data into major taxonomic groups. The limited accuracy of these methods can require significant manual post-processing to correct the automatically generated results, in order to obtain accurate estimates of plankton abundance patterns. We present here a dual-classification method in which each plankton image is first identified using a shaped-based feature set and a neural network classifier, and then a second time using a texture-based feature set and a support vector machine classifier. The plankton image is considered to belong to a given taxon only if the 2 identifications agree; otherwise it is labeled as unknown. This dual-classification method greatly reduces the false positive rate, and thus gives better abundance estimation in regions of low relative abundance. A confusion matrix is computed from a set of training images in order to determine the detection and false positives rates. These rates are used to correct abundances estimated from the automatic classification results. Aside from the manual sorting required to generate the initial training set of images, this dual-classification method is fully automatic and does not require subsequent manual correction of automatically sorted images. The resulting abundances agree closely with those obtained using manually sorted results. A set of images from a Video Plankton Recorder was used to evaluate this method and compare it with previously reported single-classifier results for major taxa.
Author Posting. © Inter-Research, 2006. This article is posted here by permission of Inter-Research for personal use, not for redistribution. The definitive version was published in Marine Ecology Progress Series 306 (2006): 51-61, doi:10.3354/meps306051.
Showing items related by title, author, creator and subject.
Benfield, Mark C.; Grosjean, Philippe; Culverhouse, Phil F.; Irigoien, Xabier; Sieracki, Michael E.; Lopez-Urrutia, Angel; Dam, Hans G.; Hu, Qiao; Davis, Cabell S.; Hansen, Allen; Pilskaln, Cynthia H.; Riseman, Edward M.; Schultz, Howard; Utgoff, Paul E.; Gorsky, Gabriel (Oceanography Society, 2007-06)When Victor Hensen deployed the first true plankton1 net in 1887, he and his colleagues were attempting to answer three fundamental questions: What planktonic organisms are present in the ocean? How many of each type ...
Biological-physical interactions on Georges Bank : plankton transport and population dynamics of the ocean quahog, Arctica islandica Lewis, Craig V. (Massachusetts Institute of Technology and Woods Hole Oceanographic Institution, 1997-06)Advective losses of bank water during winter because of strong wind forcing were hypothesized to be a significant factor limiting recruitment of Georges Bank cormnunities. This hypothesis was examined using biological-physical ...
Hu, Qiao (Massachusetts Institute of Technology and Woods Hole Oceanographic Institution, 2006-06)A fundamental problem in limnology and oceanography is the inability to quickly identify and map distributions of plankton. This thesis addresses the problem by applying statistical machine learning to video images ...