Since DCNNs (deep convolutional neural networks) have been successfully applied to
various academic and industrial fields, semantic segmentation methods, based on DCNNs …

Contextual information plays a pivotal role in the semantic segmentation of remote sensing
imagery (RSI) due to the imbalanced distributions and ubiquitous intra-class variants. The …

Featured Application Machine learning algorithms are essential to crop identification and
land use/cover. Our work indicates that compared to RF and CART algorithms, SVM …

Semantic segmentation plays a pivotal role in the intelligent interpretation of remote sensing
images (RSIs). However, conventional methods predominantly focus on learning …

Precision agriculture (PA) research aims to design decision systems based on agricultural
site control and management. These systems consist of observing fields and measuring …

Waterbody extraction from satellite imagery plays a crucial role in various environmental
monitoring and management applications. Accurate identification and delineation of water …

Pan-sharpening is a significant task that aims to generate high spectral-and spatial-
resolution remote-sensing image by fusing multi-spectral (MS) and panchromatic (PAN) …

Convolutional neural networks (CNNs) have significance in remote sensing image mapping,
and pixel-level representation allows refined results. Due to inconsistencies within a class …

SDGSAT-1, the first scientific satellite dedicated to advancing the United Nations 2030
Agenda for Sustainable Development, brings renewed vigor and opportunities to water …

Accurate water extraction from urban remote sensing images holds great significance in
assisting the formulation of river and lake management policies and ensuring the …