Wildfires substantially disrupt and reshape the structure, composition and functioning of
ecosystems. Monitoring post-fire recovery dynamics is crucial for evaluating resilience and …

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 …

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

Building change detection (BCD) recently can be handled well under the booming of deep-
learning based computer vision techniques. However, segmentation and recognition for …

Change detection is a critical task in studying the dynamics of ecosystems and human
activities using multi-temporal remote sensing images. While deep learning has shown …

Remote sensing image change detection is extensively utilized in various applications in the
field of remote sensing, particularly in the realm of cropland conservation, where it plays a …

The diversity of change detection (CD) methods and the limitations in generalizing these
techniques using different types of remote sensing datasets over various study areas have …

In the realm of conventional deep-learning-based pan-sharpening approaches, there has
been an ongoing struggle to harmonize the input panchromatic (PAN) and multi-spectral …

Semi-supervised change detection (CD) is increasingly becoming an interesting and
challenging topic for the remote sensing image processing community. As the application of …

Change detection is crucial for evaluating land use, land cover changes, and sustainable
development, constituting a significant component of Earth observation tasks. The difficulty …