Land surface temperature and emissivity retrieval from thermal infrared hyperspectral imaging

A new algorithm, optimized land surface temperature and emissivity retrieval (OLSTER), is presented to compensate for atmospheric effects and retrieve land surface temperature (LST) and emissivity from airborne thermal infrared hyperspectral data. The OLSTER algorithm is designed to retrieve properties of both natural and man-made materials. Multi-directional or multi-temporal observations are not required, and the scenes do not have to be dominated by blackbody features. The OLSTER algorithm consists of a preprocessing step, an iterative search for nearblackbody pixels, and an iterative constrained optimization loop. The preprocessing step provides initial estimates of LST per pixel and the atmospheric parameters of transmittance and upwelling radiance for the entire image. Pixels that are under- or overcompensated by the estimated atmospheric parameters are classified as near-blackbody and lower emissivity pixels, respectively. A constrained optimization of the atmospheric parameters using generalized reduced gradients on the near-blackbody pixels ensures physical results. The downwelling radiance is estimated from the upwelling radiance by applying a look-up table of coefficients based on a polynomial regression of radiative transfer model runs for the same sensor altitude. The LST and emissivity per pixel are retrieved simultaneously using the well established ISSTES algorithm. The OLSTER algorithm retrieves land surface temperatures within about ± 1.0 K, and emissivities within about ± 0.01 based on numerical simulation and validation work comparing results from sensor data with ground truth measurements. The OLSTER algorithm is currently one of only a few algorithms available that have been documented to retrieve accurate land surface temperatures and absolute land surface spectral emissivities from passive airborne hyperspectral LWIR sensor imagery

Remote Sensing Monitoring of Land Surface Temperature (LST)

This book is a collection of recent developments, methodologies, calibration and validation techniques, and applications of thermal remote sensing data and derived products from UAV-based, aerial, and satellite remote sensing. A set of 15 papers written by a total of 70 authors was selected for this book. The published papers cover a wide range of topics, which can be classified in five groups: algorithms, calibration and validation techniques, improvements in long-term consistency in satellite LST, downscaling of LST, and LST applications and land surface emissivity research

Learning Set Representations for LWIR In-Scene Atmospheric Compensation

Atmospheric compensation of long-wave infrared (LWIR) hyperspectral imagery is investigated in this article using set representations learned by a neural network. This approach relies on synthetic at-sensor radiance data derived from collected radiosondes and a diverse database of measured emissivity spectra sampled at a range of surface temperatures. The network loss function relies on LWIR radiative transfer equations to update model parameters. Atmospheric predictions are made on a set of diverse pixels extracted from the scene, without knowledge of blackbody pixels or pixel temperatures. The network architecture utilizes permutation-invariant layers to predict a set representation, similar to the work performed in point cloud classification. When applied to collected hyperspectral image data, this method shows comparable performance to Fast Line-of-Sight Atmospheric Analysis of Hypercubes-Infrared (FLAASH-IR), using an auto- mated pixel selection approach. Additionally, inference time is significantly reduced compared to FLAASH-IR with predictions made on average in 0.24 s on a 128 pixel by 5000 pixel data cube using a mobile graphics card. This computational speed-up on a low-power platform results in an autonomous atmospheric compensation method effective for real-time, onboard use, while only requiring a diversity of materials in the scene

Passively Estimating Index of Refraction for Specular Reflectors Using Polarimetric Hyperspectral Imaging

As off-nadir viewing platforms becoming increasingly prevalent in remote sensing, material classification and ID techniques robust to changing viewing geometries must be developed. Traditionally, either reflectivity or emissivity are used for classification, but these quantities vary with viewing angle. Instead, estimating index of refraction may be advantageous as it is invariant with respect to viewing geometry. This work focuses on estimating index of refraction from LWIR (875-1250 wavenumbers) polarimetric hyperspectral radiance measurements

Temperature - Emissivity separation assessment in a sub-urban scenario

In this paper, a methodology that aims at evaluating the effectiveness of different TES strategies is presented. The methodology takes into account the specific material of interest in the monitored scenario, sensor characteristics, and errors in the atmospheric compensation step. The methodology is proposed in order to predict and analyse algorithms performances during the planning of a remote sensing mission, aimed to discover specific materials of interest in the monitored scenario. As case study, the proposed methodology is applied to a real airborne data set of a suburban scenario. In order to perform the TES problem, three state-of-the-art algorithms, and a recently proposed one, are investigated: Temperature-Emissivity Separation'98 (TES-98) algorithm, Stepwise Refining TES (SRTES) algorithm, Linear piecewise TES (LTES) algorithm, and Optimized Smoothing TES (OSTES) algorithm. At the end, the accuracy obtained with real data, and the ones predicted by means of the proposed methodology are compared and discussed

Oblique Longwave Infrared Atmospheric Compensation

This research introduces two novel oblique longwave infrared atmospheric compensation techniques for hyperspectral imagery, Oblique In-Scene Atmospheric Compensation (OISAC) and Radiance Detrending (RD). Current atmospheric compensation algorithms have been developed for nadir-viewing geometries which assume that every pixel in the scene is affected by the atmosphere in nearly the same manner. However, this assumption is violated in oblique imaging conditions where the transmission and path radiance vary continuously as a function of object-sensor range, negatively impacting current algorithms in their ability to compensate for the atmosphere. The techniques presented here leverage the changing viewing conditions to improve rather than hinder atmospheric compensation performance. Initial analyses of both synthetic and measured hyperspectral images suggest improved performance in oblique viewing conditions compared to standard techniques. OISAC is an extension of ISAC, a technique that has been used extensively for LWIR AC applications for over 15 years, that has been developed to incorporate the range-dependence of atmospheric transmission and path radiance in identification of the atmospheric state. Similar to ISAC, OISAC requires the existence of near blackbody-like materials over the 11.73 micrometer water band. RD is a newer technique which features unsupervised classification of materials and identifies the atmospheric state which best detrends the observed radiance across all classes of materials, including those of low emissivity

Physics-constrained Hyperspectral Data Exploitation Across Diverse Atmospheric Scenarios

Hyperspectral target detection promises new operational advantages, with increasing instrument spectral resolution and robust material discrimination. Resolving surface materials requires a fast and accurate accounting of atmospheric effects to increase detection accuracy while minimizing false alarms. This dissertation investigates deep learning methods constrained by the processes governing radiative transfer to efficiently perform atmospheric compensation on data collected by long-wave infrared (LWIR) hyperspectral sensors. These compensation methods depend on generative modeling techniques and permutation invariant neural network architectures to predict LWIR spectral radiometric quantities. The compensation algorithms developed in this work were examined from the perspective of target detection performance using collected data. These deep learning-based compensation algorithms resulted in comparable detection performance to established methods while accelerating the image processing chain by 8X

New Approaches in Airborne Thermal Image Processing for Landscape Assessment

Letecká termální hyperspektrální data přinášejí řadu informací o teplotě a emisivitě zemského povrchu. Při odhadování těchto parametrů z dálkového snímání tepelného záření je třeba řešit nedourčený systém rovnic. Bylo navrhnuto několik přístupů jak tento problém vyřešit, přičemž nejrozšířenější je algoritmus označovaný jako Temperature and Emissivity Separation (TES). Tato práce má dva hlavní cíle: 1) zlepšení algoritmu TES a 2) jeho implementaci do procesingového řetězce pro zpracování obrazových dat získaných senzorem TASI. Zlepšení algoritmu TES je možné dosáhnout nahrazením používaného modulu normalizování emisivity (tzv. Normalized Emissivity Module) částí, která je založena na vyhlazení spektrálních charakteristik nasnímané radiance. Nový modul je pak označen jako Optimized Smoothing for Temperature Emissivity Separation (OSTES). Algoritmus OSTES je připojen k procesingovému řetězci pro zpracování obrazových dat ze senzoru TASI. Testování na simulovaných datech ukázalo, že použití algoritmu OSTES vede k přesnějším odhadům teploty a emisivity. OSTES byl dále testován na datech získaných ze senzorů ASTER a TASI. V těchto případech však není možné pozorovat výrazné zlepšení z důvodu nedokonalých atmosférických korekcí. Nicméně hodnoty emisivity získané algoritmem OSTES vykazují více homogenní vlastnosti než hodnoty ze standardního produktu senzoru ASTER.Airborne thermal hyperspectral data delivers valuable information about the temperature and emissivity of the Earth's surface. However, attempting to derive temperature and emissivity from remotely sensed thermal radiation results in an underdetermined system of equations. Several approaches have been suggested to overcome this problem, but the most widespread one is called the Temperature and Emissivity Separation (TES) algorithm. This work focuses on two major topics: 1) improving the TES algorithm and 2) implementing it in a processing chain of image data acquired from the TASI sensor. The improvement of the TES algorithm is achieved by replacing the Normalized Emissivity Module with a new module, which is based on smoothing of spectral radiance signatures. The improved TES algorithm is called Optimized Smoothing for Temperature Emissivity Separation (OSTES). The OSTES algorithm is appended to a pre-processing chain of image data acquired from the TASI sensor. The testing of OSTES with simulated data shows that OSTES produces more accurate and precise temperature and emissivity retrievals. OSTES was further applied on ASTER standard products and on TASI image data. In both cases is not possible to observe significant improvement of the OSTES algorithm due to imperfect atmospheric corrections. However, the OSTES emissivitites are smoother than emissivities delivered as ASTER standard product over homogeneous surfaces.