Mamba is emerging as a novel approach to overcome the challenges faced by
Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) in computer vision …

Semantic segmentation is a vital task in the field of remote sensing (RS). However,
conventional convolutional neural network (CNN) and transformer-based models face …

Semantic segmentation of high-resolution remote sensing images is vital in downstream
applications such as land-cover mapping, urban planning, and disaster assessment …

Local and global spectral and spatial information is crucial for hyperspectral image (HSI)
classification. However, modeling the global context has been challenging due to the …

This study was designed to address the challenges of autonomous navigation facing UAVs
in urban air mobility environments without GPS. Unlike traditional localization methods that …

Semantic segmentation is an important computer vision task due to its numerous real-world
applications such as autonomous driving, video surveillance, medical image analysis …

Semantic segmentation plays a crucial role in remote sensing applications, where the
accurate extraction and representation of features are essential for high-quality results …

Medical image segmentation plays a vital role in assisting doctors to quickly and accurately
identify pathological areas in medical images, which is essential for effective diagnosis and …

Remote sensing semantic segmentation is a critical technology in the field of remote sensing
image processing, with broad applications in environmental monitoring, urban planning …

We focus on the complexity and multi-scale importance of semantic segmentation of remote
sensing images, a fundamental task in earth science research. We propose an architecture …