Oil spill GF-1 remote sensing image segmentation using an evolutionary feedforward neural network

Jianchao Fan, Dongzhi Zhao, Jun Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)

Abstract

To improve self-made satellites in the marine oil spill monitoring accuracy, it is presented that a Gao Fen (GF-1) satellite marine oil spill remote sensing (RS) image classification algorithm based on a novel evolutionary neural network. First, a non-negative matrix factorization (NMF) algorithm is employed to extract the image features. Compared with basic features, such as the image spectrum and texture, structuring more targeted oil spill image localization non-negative character fits better for the physical significance of remote sensing images. Furthermore, on the basis of the new features, a new feedforward neural network structure with particle swarm optimization (PSO) algorithm is proposed for GF-1 RS image segmentation. Simulation results of the oil spill event substantiate the effectiveness of the proposed approach to GF-1 satellite image segmentation.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages460-464
Number of pages5
ISBN (Electronic)9781479914845
DOIs
Publication statusPublished - 3 Sep 2014
Externally publishedYes
Event2014 International Joint Conference on Neural Networks, IJCNN 2014 - Beijing, China
Duration: 6 Jul 201411 Jul 2014

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Conference

Conference2014 International Joint Conference on Neural Networks, IJCNN 2014
Country/TerritoryChina
CityBeijing
Period6/07/1411/07/14

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