2026-04-29T02:37:57Zhttps://repisalud.isciii.es/rest/oai/request

oai:repisalud.isciii.es:20.500.12105/203982024-07-11T09:07:30Zcom_20.500.12105_15322com_20.500.12105_2051col_20.500.12105_16967

00925njm 22002777a 4500 dc Bonin-Font, Francisco author Burguera, Antoni author Lisani, Jose-Luis author 2017 Controlling and quantifying the presence of Posidonia Oceanica (P.O.) in the Mediterranean sea is crucial for the conservation of these endemic ecosystems and to underscore the negative impact of many anthropogenic activities. These activities, which include uncontrolled leisure anchoring or illegal drag fishing, directly affect the tourism and fishing industries. Nowadays, the control and quantification of P.O. is done by divers, in a slow and imprecise process achieved in successive missions of a duration limited by the capacity of the oxygen scuba tanks. This paper proposes the application of robotic and computer vision technologies to upgrade the current P.O. control methods, building large scale coverage maps using the imagery provided by an autonomous underwater vehicle endowed with a bottom-looking camera. The process includes four main steps: 1) training a classifier based on two different Gabor filter image patch descriptors and a support vector machine; 2) detecting P.O. autonomously, both on-line and off-line, in each individual image; 3) color photo-mosaicking the area explored by the vehicle to obtain a global view of the meadow structure; these mosaics are extremely useful to analyze the structure and extension of the meadow and to calculate some of the biological descriptors needed to diagnose its state; and 4) building a binary coverage map in which the classification results of areas with image overlap are fused according to four different strategies. The experiments, performed in coastal areas of Mallorca and Girona, evaluate and compare the proposed descriptors and fusion techniques, showing, in some cases, accuracies and precisions above 90% in the detection of different patterns of P.O., from video sequences at different locations, in different seasons and with different environmental conditions. Bonnin Pascual F, Burguera A, Lisani JL. Visual Discrimination and Large Area Mapping of Posidonia Oceanica Using a Lightweight AUV. IEEE Access. 2017;5:24479-94. 2169-3536 http://hdl.handle.net/20.500.13003/10015 http://hdl.handle.net/20.500.12105/20398 10.1109/ACCESS.2017.2764998 IEEE Access 2-s2.0-85032696042 417742800042 Posidonia Oceanica Gabor filters Machine learning Photo-mosaicing Autonomous Underwater vehicles Visual Discrimination and Large Area Mapping of Posidonia Oceanica Using a Lightweight AUV