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 …
Remote sensing image scene classification meets deep learning: Challenges, methods, benchmarks, and opportunities
Remote sensing image scene classification, which aims at labeling remote sensing images
with a set of semantic categories based on their contents, has broad applications in a range …
with a set of semantic categories based on their contents, has broad applications in a range …
Learning deep semantic segmentation network under multiple weakly-supervised constraints for cross-domain remote sensing image semantic segmentation
Due to its wide applications, remote sensing (RS) image semantic segmentation has
attracted increasing research interest in recent years. Benefiting from its hierarchical abstract …
attracted increasing research interest in recent years. Benefiting from its hierarchical abstract …
Domain adaptation in remote sensing image classification: A survey
Traditional remote sensing (RS) image classification methods heavily rely on labeled
samples for model training. When labeled samples are unavailable or labeled samples have …
samples for model training. When labeled samples are unavailable or labeled samples have …
[HTML][HTML] Semi-supervised bidirectional alignment for remote sensing cross-domain scene classification
Remote sensing (RS) image scene classification has obtained increasing attention for its
broad application prospects. Conventional fully-supervised approaches usually require a …
broad application prospects. Conventional fully-supervised approaches usually require a …
Universal domain adaptation for remote sensing image scene classification
The domain adaptation (DA) approaches available to date are usually not well suited for
practical DA scenarios of remote sensing image classification since these methods (such as …
practical DA scenarios of remote sensing image classification since these methods (such as …
Multisource compensation network for remote sensing cross-domain scene classification
Cross-domain scene classification refers to the scene classification task in which the training
set (termed source domain) and the test set (termed target domain) come from different …
set (termed source domain) and the test set (termed target domain) come from different …
Unsupervised domain adaptation augmented by mutually boosted attention for semantic segmentation of VHR remote sensing images
This work investigates unsupervised domain adaptation (UDA)-based semantic
segmentation of very high-resolution (VHR) remote sensing (RS) images from different …
segmentation of very high-resolution (VHR) remote sensing (RS) images from different …
Domain adaptation from daytime to nighttime: A situation-sensitive vehicle detection and traffic flow parameter estimation framework
Vehicle detection in traffic surveillance images is an important approach to obtain vehicle
data and rich traffic flow parameters. Recently, deep learning based methods have been …
data and rich traffic flow parameters. Recently, deep learning based methods have been …
Domain adaptation via a task-specific classifier framework for remote sensing cross-scene classification
The scene classification of high spatial resolution (HSR) imagery involves labeling an HSR
image with a specific high-level semantic class according to the composition of the semantic …
image with a specific high-level semantic class according to the composition of the semantic …