English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

This paper proposes an automatic detection of oil spills on SAR (synthetic aperture radar) images using DE (differential evolution), neutral network and BP (back propagation) algorithm. Here, DE and BP are combined to train a multilayer perceptron (MLP) network for achieving the global extreme with a better convergence speed. The input data of neural networks are the geometrical characteristics of oil spills (e.g. area, perimeter, complexity) and the physical behavior of oil spills (e.g. mean or max backscatter value, standard deviation of the dark formation). The out data are oil spill or look-alike. We experiment ALOS/PALSAR and EnviSAT ASAR on East sea area of Viet Nam. The experimental results show that the combination algorithm converges faster and has significantly better capability of avoiding local optima.

A Combination Method of Differential Evolution Algorithm and Neural Network for Automatic Identification Oil Spill at Vietnam East Sea