Local feature acquisition and global context understanding network for very high-resolution land cover classification.

Very high-resolution remote sensing images hold promising applications in ground observation tasks, paving the way for highly competitive solutions using image processing techniques for land cover classification. To address the challenges faced by convolutional neural network (CNNs) in exploring contextual information in remote sensing image land cover classification and the limitations of vision transformer (ViT) series in effectively capturing local details and spatial information, we propose a local feature acquisition and global context understanding network (LFAGCU). Specifically, we design a multidimensional and multichannel convolutional module to construct a local feature extractor aimed at capturing local information and spatial relationships within images.

Melatonin-induced myeloblastosis viral oncogene homologs alleviate fresh-cut lotus root browning during storage by attenuating flavonoid biosynthesis and reactive oxygen species.

Background: Lotus roots (Nelumbo nucifera Gaertn.) are rich in nutrients and have ornamental and food value. However, browning has caused huge economic losses and security risks during the storage and harvesting of fresh-cut lotus.