Deep learning for change detection in remote sensing: a review
ABSTRACT A large number of publications have incorporated deep learning in the process
of remote sensing change detection. In these Deep Learning Change Detection (DLCD) …
of remote sensing change detection. In these Deep Learning Change Detection (DLCD) …
Bi-temporal semantic reasoning for the semantic change detection in HR remote sensing images
Semantic change detection (SCD) extends the multiclass change detection (MCD) task to
provide not only the change locations but also the detailed land-cover/land-use (LCLU) …
provide not only the change locations but also the detailed land-cover/land-use (LCLU) …
An unsupervised remote sensing change detection method based on multiscale graph convolutional network and metric learning
As a fundamental application, change detection (CD) is widespread in the remote sensing
(RS) community. With the increase in the spatial resolution of RS images, high-resolution …
(RS) community. With the increase in the spatial resolution of RS images, high-resolution …
From easy to hard: Learning language-guided curriculum for visual question answering on remote sensing data
Visual question answering (VQA) for remote sensing scene has great potential in intelligent
human–computer interaction system. Although VQA in computer vision has been widely …
human–computer interaction system. Although VQA in computer vision has been widely …
Image regression with structure cycle consistency for heterogeneous change detection
Change detection (CD) between heterogeneous images is an increasingly interesting topic
in remote sensing. The different imaging mechanisms lead to the failure of homogeneous …
in remote sensing. The different imaging mechanisms lead to the failure of homogeneous …
Patch similarity graph matrix-based unsupervised remote sensing change detection with homogeneous and heterogeneous sensors
Change detection (CD) of remote sensing images is an important and challenging topic,
which has found a wide range of applications in many fields. In particular, one of the main …
which has found a wide range of applications in many fields. In particular, one of the main …
Spectral‐spatial sequence characteristics‐based convolutional transformer for hyperspectral change detection
Recently, ground coverings change detection (CD) driven by bitemporal hyperspectral
images (HSIs) has become a hot topic in the remote sensing community. There are two …
images (HSIs) has become a hot topic in the remote sensing community. There are two …
Multitemporal hyperspectral images change detection based on joint unmixing and information coguidance strategy
The richness of spectral information in multitemporal hyperspectral images (MHSIs) offers
the possibility to effectively detect subtle changes and properties of grounds. However …
the possibility to effectively detect subtle changes and properties of grounds. However …
Detecting changes by learning no changes: Data-enclosing-ball minimizing autoencoders for one-class change detection in multispectral imagery
Change detection is a long-standing and challenging problem in remote sensing. Very often,
features about changes are difficult to model beforehand, thus making the collection of …
features about changes are difficult to model beforehand, thus making the collection of …
Global and local structure network for image classification
J Wang, R Ran, B Fang - IEEE Access, 2023 - ieeexplore.ieee.org
Principal component analysis network (PCANet) is a feature learning algorithm that is widely
used in face recognition and object classification. However, original PCANet still has some …
used in face recognition and object classification. However, original PCANet still has some …