Weakly supervised object detection in remote sensing images (RSI) is still a challenge
because of the lack of instance-level labels, and many existing methods have two problems …

With the rapid acquisition of remote sensing (RS) data, new categories of objects continue to
emerge, and some categories can only obtain a few training samples. Thus, few-shot class …

Dynamic technological progress has contributed to the development of systems imaging of
the Earth's surface as well as data mining methods. One such example is super-resolution …

Aircraft classification via remote sensing images has many commercial and military
applications. The Swin-Transformer has shown great promise, recently dominating general …

Weakly supervised object detection (WSOD) in remote sensing images (RSIs) aims to detect
high-value targets by solely utilizing image-level category labels; however, two problems …

The introduction of various deep neural network architectures has greatly advanced aircraft
pose estimation using high-resolution images. However, realistic airport surface monitors …

Aircraft detection holds significant importance in both civilian and military fields, such as air
transportation control and battlefield situational awareness. Deep learning-based methods …

Precise aircraft recognition in low-resolution remote sensing imagery is a challenging yet
crucial task in aviation, especially combat identification. This research addresses this …

The intelligent interpretation of spaceborne SAR images is an important technical field for
future remote sensing applications. However, due to the imaging defects of SAR itself, it is …

The potential of implementing Remote sensing in terms of applications has been extended
due to the significant enhancement in the spatial and spectral resolution. One of the most …