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

Maximum Likelihood Temperature/Emissivity Separation of Hyperspectral Images with Gaussian Distributed Downwelling Radiance

Hyperspectral images are made up of energy measurements at different wavelengths of light. The case is considered where these measurements are dependent on temperature, the self-emitted energy (emissivity), and reflected energy (downwelling radiance) from the surroundings. The process where the downwelling radiance is fixed and the temperature and emissivity are estimated is referred to as temperature/emissivity separation. Due to the way these terms mix, for a given set of measurements, there exist many pairs of temperatures and emissivities that satisfy the model. This creates ambiguity in the solution that must be resolved for the result to have any significance. A new model is developed which reduces this ambiguity. This model is used to form an objective function. The temperature and emissivity which maximize the value of the objective function are solved for given a set of measurements. As part of the solution, a new algorithm is developed which exploits the shape of the objective function to estimate the temperature and emissivity quickly and accurately. Extensive testing of this algorithm is performed to gain an understanding of its average speed and accuracy

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.

Automated lithological mapping using airborne hyperspectral thermal infrared data: A case study from Anchorage Island, Antarctica

The thermal infrared portion of the electromagnetic spectrum has considerable potential for mineral and lithological mapping of the most abundant rock-forming silicates that do not display diagnostic features at visible and shortwave infrared wavelengths. Lithological mapping using visible and shortwave infrared hyperspectral data is well developed and established processing chains are available, however there is a paucity of such methodologies for hyperspectral thermal infrared data. Here we present a new fully automated processing chain for deriving lithological maps from hyperspectral thermal infrared data and test its applicability using the first ever airborne hyperspectral thermal data collected in the Antarctic. A combined airborne hyperspectral survey, targeted geological field mapping campaign and detailed mineralogical and geochemical datasets are applied to small test site in West Antarctica where the geological relationships are representative of continental margin arcs. The challenging environmental conditions and cold temperatures in the Antarctic meant that the data have a significantly lower signal to noise ratio than is usually attained from airborne hyperspectral sensors. We applied preprocessing techniques to improve the signal to noise ratio and convert the radiance images to ground leaving emissivity. Following preprocessing we developed and applied a fully automated processing chain to the hyperspectral imagery, which consists of the following six steps: (1) superpixel segmentation, (2) determine the number of endmembers, (3) extract endmembers from superpixels, (4) apply fully constrained linear unmixing, (5) generate a predictive classification map, and (6) automatically label the predictive classes to generate a lithological map. The results show that the image processing chain was successful, despite the low signal to noise ratio of the imagery; reconstruction of the hyperspectral image from the endmembers and their fractional abundances yielded a root mean square error of 0.58%. The results are encouraging with the thermal imagery allowing clear distinction between granitoid types. However, the distinction of fine grained, intermediate composition dykes is not possible due to the close geochemical similarity with the country rock

Characterization of the Earth\u27s surface and atmosphere for multispectral and hyperspectral thermal imagery

The goal of this research was to develop a new approach to solve the inverse problem of thermal remote sensing of the Earth. This problem falls under a large class of inverse problems that are ill-conditioned because there are many more unknowns than observations. The approach is based on a multivariate analysis technique known as Canonical Correlation Analysis (CCA). By collecting two ensembles of observations, it is possible to find the latent dimensionality where the data are maximally correlated. This produces a reduced and orthogonal space where the problem is not ill-conditioned. In this research, CCA was used to extract atmospheric physical parameters such as temperature and water vapor profiles from multispectral and hyperspectral thermal imagery. CCA was also used to infer atmospheric optical properties such as spectral transmission, upwelled radiance, and downwelled radiance. These properties were used to compensate images for atmospheric effects and retrieve surface temperature and emissivity. Results obtained from MODTRAN simulations, the MODerate resolution Imaging Spectrometer (MODIS) Airborne Sensor (MAS), and the MODIS and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) (MASTER) airborne sensor show that it is feasible to retrieve land surface temperature and emissivity with 1.0 K and 0.01 accuracies, respectively

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

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

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