Sea fog significantly threatens the safety of maritime activities. This paper develops a sea dataset (SFDD) and dual branch detection network (DB-SFNet). We investigate all observed events in Yellow Bohai (118.1{\deg}E-128.1{\deg}E, 29.5{\deg}N-43.8{\deg}N) from 2010 to 2020, collect images for each event Geostationary Ocean Color Imager (GOCI) comprise SFDD. The location image SFDD is accurate...

Real-time semantic segmentation, which aims to achieve high segmentation accuracy at real-time inference speed, has received substantial attention over the past few years. However, many state-of-the-art methods tend sacrifice some spatial details or contextual information for fast inference, thus leading degradation in quality. In this paper, we propose a novel Deep Multi-branch Aggregation Net...

The development of remote sensing images in recent years has made it possible to identify materials inaccessible environments and study natural on a large scale. But hyperspectral (HSIs) are rich source information with their unique features various applications. However, several problems reduce the accuracy HSI classification; for example, extracted not effective, noise, correlation bands, mos...

This paper proposes a colour variation minimization retinex decomposition and enhancement with multi-branch network (CvmD-net) to remove single image darkness. The overcomes the problem that deep learning model relies on matching bright images process dark images. Specifically, our method takes two stages light up darkness in initial images: brightness optimization. We propose an input constant...

Deep convolution neural network (DCNN) technology has achieved great success in extracting buildings from aerial images. However, the current mainstream algorithms are not satisfactory feature extraction and classification of homesteads, especially complex rural scenarios. This study proposes a deep convolutional for homestead consisting detail branch, semantic boundary namely Multi-Branch Netw...