The blooming proliferation of aeronautics and astronautics platforms, together with the ever-
increasing remote sensing imaging sensors on these platforms, has led to the formation of …

Remote sensing image retrieval (RSIR) aims to search and retrieve the images of interest
from a large remote sensing image archive, which has remained to be a hot topic over the …

Pavement defect detection is of profound significance regarding road safety, so it has been a
trending research topic. In the past years, deep learning based methods have turned into a …

The discriminative ability of image features plays a decisive role in content-based remote
sensing image retrieval (CBRSIR). However, the widely-used convolutional neural networks …

With the joint advancement of numerous related fields of remote sensing, the amount of
remote sensing data is growing exponentially. As an essential remote sensing Big Data …

Deep learning-based multi-task learning (MTL) methods have recently attracted attention for
content-based image retrieval (CBIR) applications in remote sensing (RS). For a given set of …

In autonomous driving, the perception module typically utilizes a combination of millimeter-
wave radar and LiDAR. However, when driving in challenging environmental conditions, this …

The increasing volume of high-resolution satellite imagery made image retrieval a
demanding research field in the remote sensing (RS) community. The RS data are often …

This paper presents an improved Oriented Features from Accelerated Segment Test (FAST)
and Rotated BRIEF (ORB) algorithm named ORB using three-patch and local gray …

In high-resolution remote sensing image retrieval (HRRSIR), convolutional neural networks
(CNNs) have an absolute performance advantage over the traditional hand-crafted features …