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 …

Benchmark datasets are critical for developing, evaluating, and comparing remote sensing
image retrieval (RSIR) approaches. However, current benchmark datasets are deficient in …

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 …

With the explosive growth of remote sensing big data, large-scale remote sensing image
retrieval (RSIR) has become one of the most challenging tasks in data mining, attracting …

Remote sensing image retrieval (RSIR) is a fundamental task in remote sensing. Most
content-based RSIR approaches take a simple distance as similarity criteria. A retrieval …

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 …

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 …

Due to the specific characteristics and complicated contents of remote sensing (RS) images,
remote sensing image retrieval (RSIR) is always an open and tough research topic in the RS …

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

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 …