Multimodal image matching, which refers to identifying and then corresponding the same or
similar structure/content from two or more images that are of significant modalities or …

Deep learning (DL) algorithms have seen a massive rise in popularity for remote-sensing
image analysis over the past few years. In this study, the major DL concepts pertinent to …

As a fundamental and critical task in various visual applications, image matching can identify
then correspond the same or similar structure/content from two or more images. Over the …

Registration for multisensor or multimodal image pairs with a large degree of distortions is a
fundamental task for many remote sensing applications. To achieve accurate and low-cost …

Identifying feature correspondences between multimodal images is facing enormous
challenges because of the significant differences both in radiation and geometry. To address …

Traditional feature matching methods, such as scale-invariant feature transform (SIFT),
usually use image intensity or gradient information to detect and describe feature points; …

Co-registration of multimodal remote sensing (RS) images (eg, optical, infrared, LiDAR, and
SAR) is still an ongoing challenge because of nonlinear radiometric differences (NRD) and …

In the task of change detection (CD), high-resolution remote sensing images (HRSIs) can
provide rich ground object information. However, the interference from noise and complex …

The blooming proliferation of aeronautics and astronautics platforms, together with the ever-
increasing remote sensing imaging sensors on these platforms, has led to the formation of …

While image matching has been studied in remote sensing community for decades,
matching multimodal data [eg, optical, light detection and ranging (LiDAR), synthetic …