With the rapid growth of remote sensing image data, it has become necessary to effectively
and efficiently retrieve images from a big image database for managing and exploiting such …

With the rapid development of internet technology in recent years, the available remote
sensing image data have also been growing rapidly, which has led to an increased demand …

Many image features have been proposed for image retrieval; hence, effectively fusing these
features to alleviate the large variation in performance among image queries when using …

The content-based remote sensing image retrieval (CBRSIR) has attracted increasing
attention with the number of remote sensing (RS) images growing explosively. Benefiting …

The rising number and size of remote sensing (RS) image archives makes content-based
RS image retrieval (CBRSIR) more important. Convolutional neural networks (CNNs) offer …

In the field of content-based remote sensing (RS) image retrieval, convolutional neural
networks (CNNs) have been demonstrating overwhelming superiority among other methods …

In recent years, the amount of remote sensing imagery data has increased exponentially.
The ability to quickly and effectively find the required images from massive remote sensing …

Effective feature representations play a decisive role in content-based remote sensing
image retrieval (CBRSIR). Recently, learning-based features have been widely used in …

Benchmark datasets play an important role in evaluating remote sensing image retrieval
(RSIR) methods. The current datasets cover many scene categories, but omit an important …

Recently, deep metric learning (DML) has received widespread attention in the field of
remote sensing image retrieval (RSIR), owing to its ability to extract discriminative features to …