Monitoring changes in the Earth's surface is crucial for understanding natural processes and
human impacts, necessitating precise and comprehensive interpretation methodologies …

Remote sensing image change captioning (RSICC) is a novel task that aims to describe the
differences between bitemporal images by natural language. Previous methods ignore a …

Most contemporary supervised remote sensing (RS) image change detection (CD)
approaches are customized for equal-resolution bitemporal images. Real-world applications …

The existing Remote Sensing Image Change Captioning (RSICC) methods perform well in
simple scenes but exhibit poorer performance in complex scenes. This limitation is primarily …

Deep neural networks (DNNs) have been widely applied in remote sensing, and the
research on its adversarial attack algorithm is the key to evaluating its robustness. Current …

Building extraction from remote sensing images is a hot topic in the fields of computer vision
and remote sensing. In recent years, driven by deep learning, the accuracy of building …

Multi-view segmentation in Remote Sensing (RS) seeks to segment images from diverse
perspectives within a scene. Recent methods leverage 3D information extracted from an …

In the domain of photogrammetry, the fusion of information from multiple views holds the
potential to significantly enhance the accuracy and robustness of building map**. While …

Noticeable achievements have been made in entity-level perception tasks (eg., object
detection) in remote sensing image interpretation. But for remote sensing images carrying …

Change detection (CD) in high-resolution remote sensing images (RSIs) aims at locating
and understanding surface change areas. Despite some models have been proposed to …