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 …

Cross-modal hashing is an important tool for retrieving useful information from very-high-
resolution (VHR) optical images and synthetic aperture radar (SAR) images. Dealing with …

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 …

Unsupervised hashing for remote sensing (RS) image retrieval first extracts image features
and then uses these features to construct supervised information (eg, pseudolabels) to train …

In existing remote sensing image retrieval (RSIR) datasets, the number of images among
different classes varies dramatically, which leads to a severe class imbalance problem …

In extreme off-road scenarios, autonomous driving technology holds strategic significance
for enhancing emergency rescue capabilities, reducing labor intensity, and mitigating safety …

This work introduces composed image retrieval to remote sensing. It allows to query a large
image archive by image examples alternated by a textual description, enriching the …

Deep unsupervised hashing methods are gaining attention in the field of remote sensing
(RS) image retrieval due to the rapid growth in the volume of unlabeled RS data. Most …

In the geological research of the Moon and other celestial bodies, the identification and
analysis of impact craters are crucial for understanding the geological history of these …