Emissivity correction using spectrum correlation of infrared and visible images

Fusion of Thermal Infrared and Visible Images Based on Multi-scale Transform and Sparse Representation

Due to the differences between the visible and thermal infrared images, combination of these two types of images is essential for better understanding the characteristics of targets and the environment. Thermal infrared images have most importance to distinguish targets from the background based on the radiation differences, which work well in all-weather and day/night conditions also in land s...

Fusion of Infrared and Visible-Light Images

Registration of visible and infrared images

This paper presents an approach for registering images obtained from sensors tuned to significantly different regions of the electromagnetic spectrum. Standard correlation techniques for registration are not appropriate because of the inherent differences between the images. We present an approach which uses surface boundaries ( obtained at multiple scales via an edge extraction process) to det...

Spatial-Spectral-Emissivity Land-Cover Classification Fusing Visible and Thermal Infrared Hyperspectral Imagery

High-resolution visible remote sensing imagery and thermal infrared hyperspectral imagery are potential data sources for land-cover classification. In this paper, in order to make full use of these two types of imagery, a spatial-spectral-emissivity land-cover classification method based on the fusion of visible and thermal infrared hyperspectral imagery is proposed, namely, SSECRF (spatial-spe...

Fusion of Infrared and Visible Images for Face Recognition

A number of studies have demonstrated that infrared (IR) imagery offers a promising alternative to visible imagery due to it’s insensitive to variations in face appearance caused by illumination changes. IR, however, has other limitations including that it is opaque to glass. The emphasis in this study is on examining the sensitivity of IR imagery to facial occlusion caused by eyeglasses. Our e...