Automatic Habitat Mapping using Convolutional Neural Networks

• Type: Article in International Conference Proceedings Book
• Date: 2018

Authors

Diegues, A (Author) Other; José Queirós Pinto (Author) FEUP; Pedro Ribeiro (Author) FCUP; Frias, R (Author) Other; Alegre, DC (Author) Other

Conference proceedings International

Title: AUV 2018 - 2018 IEEE/OES Autonomous Underwater Vehicle Workshop, Proceedings

2018 IEEE/OES Autonomous Underwater Vehicle Workshop, AUV 2018

6 November 2018 through 9 November 2018

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 10 Citations

Other information

Authenticus ID: P-00Q-SCH

DOI: 10.1109/auv.2018.8729787

Abstract (EN): Habitat mapping is an important task to manage ecosystems. This task becomes most challenging when it comes to marine habitats as it is hard to get good images in underwater conditions and to precisely locate them. In this paper we present a novel technique for performing habitat mapping automating all phases, from data collection to classification, lowering costs and increasing efficiency throughout the process. For mapping habitats in a vast coastal region, we use visible light cameras mounted on autonomous underwater vehicles, capable of collecting and geo-locating all acquired data. The optic images are enhanced using Computer Vision techniques, to help specialists identify the habitats they contain (during training phase). In a later stage, we employ convolutional neural networks to automatically identify habitats in all imagery. Habitats are classified according to the European Nature Information System, an European classification standard for habitats.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6