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

Jianchao Fan, Dongzhi Zhao, Jun Wang

Результат исследований: Глава в книге, отчете, сборнике статейМатериалы для конференциирецензирование

5 Цитирования (Scopus)

Аннотация

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.

Язык оригиналаАнглийский
Название основной публикацииProceedings of the International Joint Conference on Neural Networks
ИздательInstitute of Electrical and Electronics Engineers Inc.
Страницы460-464
Число страниц5
ISBN (электронное издание)9781479914845
DOI
СостояниеОпубликовано - 3 сент. 2014
Опубликовано для внешнего пользованияДа
Событие2014 International Joint Conference on Neural Networks, IJCNN 2014 - Beijing, Китай
Продолжительность: 6 июл. 201411 июл. 2014

Серия публикаций

НазваниеProceedings of the International Joint Conference on Neural Networks

Конференция

Конференция2014 International Joint Conference on Neural Networks, IJCNN 2014
Страна/TерриторияКитай
ГородBeijing
Период6/07/1411/07/14

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