Remote sensing (RS) image retrieval is of great significant for geological information mining.
Over the past two decades, a large amount of research on this task has been carried out …

Remote sensing (RS) image retrieval based on visual content is of great significance for
geological information mining. Over the past two decades, a large amount of research on …

Geometric and spectral distortions of remote sensing images are key obstacles for deep
learning-based supervised classification and retrieval, which are worsened by cross-dataset …

For deep networks, accurate image similarities cannot be well characterized with limited
iterations, so the latent relationships between images can be embedded to enhance image …

Learning powerful feature representations for image retrieval has always been a
challenging task in the field of remote sensing. Traditional methods focus on extracting low …

Remote sensing images are featured by massiveness, diversity and complexity. These
features put forward higher requirements for the speed and accuracy of remote sensing …

Convolutional neural networks (CNNs) have proved to achieve state-of-the-art performance
in content-based remote sensing image retrieval (CBRSIR). However, CNNs cannot focus …

In the burgeoning remote sensing image data era, the swift and precise retrieval of images
from extensive databases has emerged as a critical challenge. This need is particularly …

Benchmark datasets are essential to develop and evaluate remote sensing image retrieval
(RSIR) approaches. However, there are no two datasets with different remote sensing image …

Remote sensing image retrieval (RSIR) is the process of searching for identical areas by
investigating the similarities between a query image and the database images. RSIR is a …