Coastlines are important basic geographic elements and mapping their spatial and attribute
changes can help monitor, model and manage coastal zones. Traditional studies focused on …

Hyperspectral image (HSI) consists of abundant spectral and spatial characteristics, which
contribute to a more accurate identification of materials and land covers. However, most …

Graph convolutional network (GCN) as a combination of deep learning (DL) and graph
learning has gained increasing attention in hyperspectral image (HSI) classification …

The accurate extraction and mapping of floating raft aquaculture (FRA) is significant to the
scientific management and sustainable development of coastal zones. However, the current …

Since 1971, remote sensing techniques have been used to map and monitor phenomena
and parameters of the coastal zone. However, updated reviews have only considered one …

Deep learning (DL) has brought many new trends for hyperspectral image classification
(HIC). Graph neural networks (GNNs) are models that fuse DL and structured data. Although …

The acquisition of dynamic information on the coastline is of great significance for the
Zhoushan archipelago. However, a large amount of suspended sediments, a tortuous …

Increased human activity and dynamic climate variables have radically altered the global
land use land cover (LULC) pattern and the ecosystem services (ES) they offer. However …

Coastal wetlands mapping is a big challenge in remote sensing fields because of similar
spectrum of different ground objects and their severe fragmentation and spatial …

In recent years, with the rapid advancement of China's urbanization, the contradiction
between urban development and the ecological environment has become increasingly …