Images gathered from different satellites are vastly available these days due to the fast
development of remote sensing (RS) technology. These images significantly enhance the …

Change Detection (CD) provides a research basis for environmental monitoring, urban
expansion and reconstruction as well as disaster assessment, by identifying the changes of …

In the change detection (CD) task, the UNet architecture has achieved superior results.
However, due to the inherent limitation of convolution operations, UNet is inadequate in …

For hyperspectral image (HSI) change detection (CD), multiscale features are usually used
to construct the detection models. However, the existing studies only consider the multiscale …

Remote sensing image change detection (CD) is done to identify desired significant
changes between bitemporal images. Given two co-registered images taken at different …

Change detection is a basic task of remote sensing image processing. The research
objective is to identify the change information of interest and filter out the irrelevant change …

This study presents a new end-to-end change detection network, called difference-
enhancement dense-attention convolutional neural network (DDCNN), that is designed for …

Temporal data contain a wealth of valuable information, playing an essential role in various
machine-learning tasks. Slow feature analysis (SFA), one of the most classic temporal …

In the task of change detection (CD), high-resolution remote sensing images (HRSIs) can
provide rich ground object information. However, the interference from noise and complex …

In recent years, vector-based machine learning algorithms, such as random forests, support
vector machines, and 1-D convolutional neural networks, have shown promising results in …