Wide-range multiscale object detection for multispectral scene perception from a drone
perspective is challenging. Previous RGB-T perception methods directly use backbone …

Self-supervised learning guided by masked image modeling, such as masked autoencoder
(MAE), has attracted wide attention for pretraining vision transformers in remote sensing …

The pretrained vision-language models (VLMs) have achieved outstanding performance in
various visual tasks, primarily due to the knowledge they have acquired from massive image …

With rising global temperatures, wildfires frequently occur worldwide during the summer
season. The timely detection of these fires, based on unmanned aerial vehicle (UAV) …

Vision-based deep-learning methods are widely employed for Surface Defect Detection
(SDD). However, as learning often needs extensive high-precision annotations, the practical …

Object detection in remote sensing scenarios plays an indispensable and significant role in
civilian, commercial, and military areas, leveraging the power of convolutional neural …

The vision transformer (ViT) has demonstrated performance comparable to that of
convolutional neural networks (CNN) in the hyperspectral image classification domain. This …

With a spurt of progress in deep learning techniques, convolutional neural network-based
and transformer-based methods have yielded impressive performance on the hyperspectral …

In the realm of urban development, the precise classification and identification of land types
are crucial for improving land use efficiency. This article proposes a land recognition and …

Hyperspectral imaging offers manifold opportunities for applications that may not or only
partially be achieved within the visual spectrum. Our paper presents a novel approach for …