The advancement in the field of remote sensing (RS) has offered a vast number of RS
images with higher resolution. Nowadays, remote sensing image retrieval (RSIR) has …

Image retrieval techniques are becoming famous due to the vast availability of multimedia
data. The present image retrieval system performs excellently on labeled data. However …

Several visual features have been developed for content-based image retrieval in the last
decades, including global, local and deep learning-based approaches. However, despite …

With the urgent demand for automatic management of large numbers of high-resolution
remote sensing images, content-based high-resolution remote sensing image retrieval (CB …

Current metric learning approaches for image retrieval are usually based on learning a
space of informative latent representations where simple approaches such as the cosine …

As different kinds of high-resolution remote sensing (HRRS) image data sources increase,
the cross-source content-based image retrieval (CS-CBRSIR) is becoming an important and …

The advent of deep perceptual networks brought about a paradigm shift in machine vision
and image perception. Image apprehension lately carried out by hand-crafted features in the …

Remote Sensing Image Retrieval remains a challenging topic due to the special nature of
Remote Sensing imagery. Such images contain various different semantic objects, which …

Remote sensing image retrieval is to find the most identical or similar images to a query
image in the vast archive of remote sensing images. A key process is to extract the most …

In remote sensing (RS) community, RSIR (Remote Sensing Image Retrieval) is considered
as a tough topic and gained more attention because the data is collected via EO (Earth …