Artificial intelligence for remote sensing data analysis: A review of challenges and opportunities
Artificial intelligence (AI) plays a growing role in remote sensing (RS). Applications of AI,
particularly machine learning algorithms, range from initial image processing to high-level …
particularly machine learning algorithms, range from initial image processing to high-level …
[HTML][HTML] MCRN: A multi-source cross-modal retrieval network for remote sensing
Cross-modal remote sensing retrieval (RSCR) has an increasing importance due to the
ability to quickly and flexibly retrieve valuable data from enormous remote sensing (RS) …
ability to quickly and flexibly retrieve valuable data from enormous remote sensing (RS) …
Attention consistent network for remote sensing scene classification
Remote sensing (RS) image scene classification is an important research topic in the RS
community, which aims to assign the semantics to the land covers. Recently, due to the …
community, which aims to assign the semantics to the land covers. Recently, due to the …
EMTCAL: Efficient multiscale transformer and cross-level attention learning for remote sensing scene classification
In recent years, convolutional neural network (CNN)-based methods have been widely used
for remote sensing (RS) scene classification tasks and have achieved excellent results …
for remote sensing (RS) scene classification tasks and have achieved excellent results …
Wnet: W-shaped hierarchical network for remote sensing image change detection
Change detection (CD) is a hot research topic in the remote-sensing (RS) community. With
the increasing availability of high-resolution (HR) RS images, there is a growing demand for …
the increasing availability of high-resolution (HR) RS images, there is a growing demand for …
Meta-hashing for remote sensing image retrieval
With the explosive growth of the volume and resolution of high-resolution remote-sensing
(HRRS) images, the management of them becomes a challenging task. The traditional …
(HRRS) images, the management of them becomes a challenging task. The traditional …
SAGN: Semantic-aware graph network for remote sensing scene classification
The scene classification of remote sensing (RS) images plays an essential role in the RS
community, aiming to assign the semantics to different RS scenes. With the increase of …
community, aiming to assign the semantics to different RS scenes. With the increase of …
Deep low-rank prior for hyperspectral anomaly detection
Hyperspectral anomaly detection is aimed at detecting observations that differ from their
surroundings. To achieve this goal, low-rank models and autoencoders (AEs) have attracted …
surroundings. To achieve this goal, low-rank models and autoencoders (AEs) have attracted …
Remote sensing image retrieval in the past decade: Achievements, challenges, and future directions
Remote sensing image retrieval (RSIR) aims to search and retrieve the images of interest
from a large remote sensing image archive, which has remained to be a hot topic over the …
from a large remote sensing image archive, which has remained to be a hot topic over the …
Homo–heterogenous transformer learning framework for RS scene classification
Remote sensing (RS) scene classification plays an essential role in the RS community and
has attracted increasing attention due to its wide applications. Recently, benefiting from the …
has attracted increasing attention due to its wide applications. Recently, benefiting from the …