Quantities of multi-temporal remote sensing (RS) images create favorable conditions for
exploring the urban change in the long term. However, diverse multi-source features and …

Synthetic aperture radar (SAR) image change detection is a vital yet challenging task in the
field of remote sensing image analysis. Most previous works adopt a self-supervised method …

Traditional synthetic-aperture radar (SAR) image change detection methods based on
convolutional neural networks (CNNs) face the challenges of speckle noise and deformation …

Deep learning methods have recently demonstrated their significant capability for synthetic
aperture radar (SAR) image change detection. However, with the increase of network depth …

Synthetic aperture radar (SAR) images can capture abundant spatial and polarimetric
information of land cover objects, and thus polarimetric SAR (PolSAR) image classification …

Benefited from the rapid and sustainable development of synthetic aperture radar (SAR)
sensors, change detection from SAR images has received increasing attentions over the …

The Sentinel-1 mission provides a freely accessible opportunity for urban image
interpretation based on synthetic aperture radar (SAR) data with a specific resolution, which …

Convolutional neural networks (CNNs) have been widely used in change detection of
synthetic aperture radar (SAR) images and have been proven to have better precision than …

Convolutional neural networks (CNNs) have been widely used in SAR image recognition
and have achieved high recognition accuracy on some public datasets. However, due to the …

The mainstream methods for change detection in synthetic-aperture radar (SAR) images use
difference images to define the initial change regions. However, methods can suffer from …