A review of deep-learning methods for change detection in multispectral remote sensing images
EJ Parelius - Remote Sensing, 2023 - mdpi.com
Remote sensing is a tool of interest for a large variety of applications. It is becoming
increasingly more useful with the growing amount of available remote sensing data …
increasingly more useful with the growing amount of available remote sensing data …
Spatial–spectral attention network guided with change magnitude image for land cover change detection using remote sensing images
Land cover change detection (LCCD) using remote sensing images (RSIs) plays an
important role in natural disaster evaluation, forest deformation monitoring, and wildfire …
important role in natural disaster evaluation, forest deformation monitoring, and wildfire …
On improving bounding box representations for oriented object detection
Detecting objects in remote sensing images (RSIs) using oriented bounding boxes (OBBs) is
flourishing but challenging, wherein the design of OBB representations is the key to …
flourishing but challenging, wherein the design of OBB representations is the key to …
Instance-aware distillation for efficient object detection in remote sensing images
Practical applications ask for object detection models that achieve high performance at low
overhead. Knowledge distillation demonstrates favorable potential in this case by …
overhead. Knowledge distillation demonstrates favorable potential in this case by …
[HTML][HTML] Deep learning change detection techniques for optical remote sensing imagery: Status, perspectives and challenges
Change detection (CD) aims to compare and analyze images of identical geographic areas
but different dates, whereby revealing spatio-temporal change patterns of Earth's surface …
but different dates, whereby revealing spatio-temporal change patterns of Earth's surface …
SFRNet: Fine-grained oriented object recognition via separate feature refinement
Fine-grained oriented object recognition (FGO) is a practical need for intellectually
interpreting remote sensing images. It aims at realizing fine-grained classification and …
interpreting remote sensing images. It aims at realizing fine-grained classification and …
Global rectification and decoupled registration for few-shot segmentation in remote sensing imagery
Few-shot segmentation (FSS), which aims to determine specific objects in the query image
given only a handful of densely labeled samples, has received extensive academic attention …
given only a handful of densely labeled samples, has received extensive academic attention …
Progressive parsing and commonality distillation for few-shot remote sensing segmentation
In recent years, few-shot segmentation (FSS) has received widespread attention from
scholars by virtue of its superiority in low-data regimes. Most existing research focuses on …
scholars by virtue of its superiority in low-data regimes. Most existing research focuses on …
Novel piecewise distance based on adaptive region key-points extraction for LCCD with VHR remote-sensing images
Land cover change detection (LCCD) with very high-resolution remote-sensing images
(VHR_RSIs) is important in observing surface change on Earth. However, pseudo-changes …
(VHR_RSIs) is important in observing surface change on Earth. However, pseudo-changes …
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 …