Geometric registration is often an accuracy assurance for most remote sensing image
processing and analysis, such as image mosaicking, image fusion, and time-series analysis …

Over the last two decades, there has been a significant improvement in the quality and
volume of the remote-sensing images. With the extensive availability of the images, these …

Infrared images can distinguish targets from their backgrounds based on the radiation
difference, which works well in all-weather and all-day/night conditions. By contrast, visible …

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

Automatic registration of multimodal remote sensing data [eg, optical, light detection and
ranging (LiDAR), and synthetic aperture radar (SAR)] is a challenging task due to the …

Image matching is a key preprocessing step for the integrated application of synthetic
aperture radar (SAR) and optical images. Due to significant nonlinear intensity differences …

The scale-invariant feature transform algorithm and its many variants are widely used in
feature-based remote sensing image registration. However, it may be difficult to find enough …

Feature matching, which refers to establishing reliable correspondences between two sets
of feature points, is a critical prerequisite in feature-based image registration. This paper …

Feature matching, which refers to establishing reliable correspondence between two sets of
features (particularly point features), is a critical prerequisite in feature-based registration. In …

Although the scale-invariant feature transform (SIFT) algorithm has been successfully
applied to both optical image registration and synthetic aperture radar (SAR) image …