Junyu Dong

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.

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.

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.

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.

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.