J
Junyu Dong
Researcher at Ocean University of China
Publications - 484
Citations - 6570
Junyu Dong is an academic researcher from Ocean University of China. The author has contributed to research in topics: Computer science & Feature extraction. The author has an hindex of 30, co-authored 399 publications receiving 3553 citations. Previous affiliations of Junyu Dong include Qingdao University.
Papers
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Journal ArticleDOI
Prediction of Sea Surface Temperature Using Long Short-Term Memory
TL;DR: This letter adopts long short-term memory (LSTM) to predict sea surface temperature (SST), and makes short- and long-term prediction, including weekly mean and monthly mean, and the model’s online updated characteristics are presented.
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Underwater image enhancement via extended multi-scale Retinex
TL;DR: This paper presents a novel method for underwater image enhancement inspired by the Retinex framework, which simulates the human visual system and utilizes the combination of the bilateral filter and trilateral filter on the three channels of the image in CIELAB color space according to the characteristics of each channel.
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Automatic Change Detection in Synthetic Aperture Radar Images Based on PCANet
TL;DR: This letter presents a novel change detection method for multitemporal synthetic aperture radar images based on PCANet that exploits representative neighborhood features from each pixel using PCA filters as convolutional filters to generate change maps with less noise spots.
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An improved NSGA-III algorithm with adaptive mutation operator for Big Data optimization problems
TL;DR: This study introduces an adaptive mutation operator to enhance the performance of the standard NSGA-III algorithm and shows results that indicate that NS GA-III with UC and adaptive mutationoperator outperforms the other NSGA -III algorithms.
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A CFCC-LSTM Model for Sea Surface Temperature Prediction
TL;DR: This letter regards SST prediction as a sequence prediction problem and builds an end-to-end trainable long short term memory (LSTM) neural network model that essentially combines the temporal and spatial information to predict future SST values.