Deep unsupervised hashing methods are gaining attention in the field of remote sensing
(RS) image retrieval due to the rapid growth in the volume of unlabeled RS data. Most …

Hashing has been widely used for large-scale remote sensing image retrieval due to its
outstanding advantages in storage and search speed. Recently, deep hashing methods …

Hashing has been widely used for large-scale image retrieval due to its high storage
efficiency and fast calculation speed. Recent works have found that deep-supervised …

Unsupervised hashing is an important approach for large-scale content-based remote
sensing (RS) image retrieval. Existing unsupervised hashing methods usually utilize data …

Remote sensing image retrieval (RSIR) refers to finding images from an image database
that contain the same instance as the query image, which is an essential task in remote …

The hashing method is widely used for large-scale image retrieval due to its low time and
space complexity. However, the existing deep hashing methods are mainly designed for …

Recently, similarity-preserving hashing methods have been extensively studied for large-
scale image retrieval. Compared with unsupervised hashing, supervised hashing methods …

Image retrieval (IR) for practical remote sensing (RS) should have high accuracy, storage,
and calculation efficiency, while not relying on big annotations. However, current supervised …

Unsupervised hashing for remote sensing (RS) image retrieval first extracts image features
and then uses these features to construct supervised information (eg, pseudolabels) to train …

Recently, the demand for remote sensing image retrieval is growing and attracting the
interest of many researchers because of the increasing number of remote sensing images …