Remote sensing image retrieval (RSIR) has broad application prospects, but related
challenges still exist. One of the most important challenges is how to obtain discriminative …

The discriminative ability of image features plays a decisive role in content-based remote
sensing image retrieval (CBRSIR). However, the widely-used convolutional neural networks …

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 …

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

With the rapid development of remote sensing sensor technology, the number of remote
sensing images (RSIs) has exploded. How to effectively retrieve and manage this massive …

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 …

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 …

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 …

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 …

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 …