Satellite imageries are an important geoinformation source for different applications in the
Earth Science field. However, due to the limitation of the optic and sensor technologies and …

For the past two decades, there have been significant efforts to develop methods for object
detection in Remote Sensing (RS) images. In most cases, the datasets for small object …

With the rise of deep convolutional neural networks, object detection has achieved
prominent advances in past years. However, such prosperity could not camouflage the …

Detecting tiny objects is one of the main obstacles hindering the development of object
detection. The performance of generic object detectors tends to drastically deteriorate on tiny …

Tiny object detection (TOD) in aerial images is challenging since a tiny object only contains
a few pixels. State-of-the-art object detectors do not provide satisfactory results on tiny …

Accurately and timely detecting multiscale small objects that contain tens of pixels from
remote sensing images (RSI) remains challenging. Most of the existing solutions primarily …

Detecting arbitrarily oriented tiny objects poses intense challenges to existing detectors,
especially for label assignment. Despite the exploration of adaptive label assignment in …

Object detection in remote sensing images (RSIs) requires the locating and classifying of
objects of interest, which is a hot topic in RSI analysis research. With the development of …

Abstract Generative Adversarial Networks (GANs) are one of the most creative advances in
Deep Learning (DL) in recent years. The Remote Sensing (RS) community has adopted …

Significant advancements and progress made in recent computer vision research enable
more effective processing of various objects in high-resolution overhead imagery obtained …