Segmentation is an essential step for remote sensing image processing. This study aims to
advance the application of the Segment Anything Model (SAM), an innovative image …

In recent years, panoptic segmentation has drawn increasing amounts of attention, leading
to the rapid emergence of numerous related algorithms. A variety of deep neural networks …

In this work, we introduce Sen4AgriNet, a Sentinel-2-based time series multicountry
benchmark dataset, tailored for agricultural monitoring applications with machine and deep …

Deep learning has gained significant attention in remote sensing, especially in pixel-or
patch-level applications. Despite initial attempts to integrate deep learning into object-based …

The automatic and accurate detection of mangroves from remote sensing data is essential to
assist in conservation strategies and decision-making that minimize possible environmental …

Vehicle classification is a hot computer vision topic, with studies ranging from ground-view to
top-view imagery. Top-view images allow understanding city patterns, traffic management …

Landcover classification is an important application in remote sensing, but it is always a
challenge to distinguish different features with similar characteristics or large-scale …

Remote sensing image segmentation plays an important role in many industrial-grade
image processing applications. However, the problem of uncertainty caused by intraclass …

Satellite remote sensing images contain complex and diverse ground object information and
the images exhibit spatial multi-scale characteristics, making the panoptic segmentation of …

Panoptic segmentation is a recent and powerful task that tackles individual object
recognition (“things”) and multiple backgrounds (“stuff”) simultaneously. Remote sensing …