Performance of the Large Field of View Airborne Infrared Scanner and its application potential in land surface temperature retrieval

The Large Field of View Airborne Infrared Scanner is a newly developed multi-spectral instrument that collects images from the near-infrared to long-wave infrared channels.Its data can be used for land surface temperature(LST)retrieval and environmental monitoring.Before data application,quality assessment is an essential procedure for a new instrument.In this paper,based on the data collected by the scanner near the Yellow River in Henan Province,the geometric and radiometric qualities of the images are first evaluated.The absolute geolocation accuracy of the ten bands of the scanner is approximately 5.1 m.The ground sampling distance is found to be varied with the whisk angles of the scanner and the spatial resolution of the images.The band-to-band registration accuracy between band one and the other nine bands is approximately 0.25 m.The length and angle deformations of the ten bands are approximately 0.67%and 0.3°,respectively.The signal-to-noise ratio(SNR)and relative radiometric calibration accuracy of bands 4,9,and 10 are relatively better than those of the other bands.Secondly,the radiative transfer equation(RTE)method is used to retrieve the LST from the data of the scanner.Measurements of in situ samples are collected to evaluate the retrieved LST.Neglecting the samples with unreasonable retrieved LST,the bias and RMSE between in situ LST measured by CE312 radiometer and retrieved LST are−0.22 K and 0.94 K,and the bias and RMSE are 0.27 K and 1.59 K for the InfReC R500-D thermal imager,respectively.Overall,the images of the Large Field of View Airborne Infrared Scanner yield a relatively satisfactory accuracy for both LST retrieval and geometric and radiometric qualities.

A new adaptive nonuniformity correction algorithm for infrared line scanner based on neural networks

The striping pattern nonuniformity of the infrared line scanner （IRLS） severely limits the system performance. An adaptive nonuniformity correction （NUC） algorithm for IRLS using neural network is proposed. It uses a one-dimensional median filter to generate ideal output of network and can complete NUC by a single frame with a high correction level. Applications to both simulated and real infrared images show that the algorithm can obtain a satisfactory result with low complexity, no need of scene diversity or global motion between consecutive frames. It has the potential to realize real-time hardware-based applications.