Fast and Effective Techniques for LWIR Radiative Transfer Modeling: A Dimension-Reduction Approach

The increasing spatial and spectral resolution of hyperspectral imagers yields detailed spectroscopy measurements from both space-based and airborne platforms. These detailed measurements allow for material classification, with many recent advancements from the fields of machine learning and deep learning. In many scenarios, the hyperspectral image must first be corrected or compensated for atmospheric effects. Radiative Transfer (RT) computations can provide look up tables (LUTs) to support these corrections. This research investigates a dimension-reduction approach using machine learning methods to create an effective sensor-specific long-wave infrared (LWIR) RT model

Metodología de validación de productos MODIS para la estimación de temperatura de la superficie en zonas heterogéneas y homogéneas de Colombia

La acelerada producción científica en geomática, ha permitido conocer fenómenos sobre la faz de la Tierra. Sus herramientas como el modelamiento espacial, los geodatos de sensores remotos, la administración de la información geoespacial y los estudios sobre la dinámica del planeta, han suministrado claves de acceso al mejoramiento del entendimiento de los fenómenos naturales y antropogénicos. El avance tecnológico provee el uso de mayor información para estudiar variables ambientales como la temperatura de la superficie (del aire a 2 metros). Sin embargo, obtener estos datos de manera tradicional involucra un costo económico y un cubrimiento espacial insuficiente, ya que esta información es requerida para adelantar estudios agronómicos expansivos, modelos matemáticos de interacción Tierra-Atmósfera y cambio climático. Frente a esta necesidad, el uso de información proveniente de datos satelitales crece a medida que los objetivos y metas en la investigación también aumentan. En Colombia, la baja cobertura espacial de las estaciones meteorológicas deja vacíos de información que pueden ser estimados a través de las herramientas geomáticas. En esta investigación, la explotación de los datos satelitales del sensor Modis en conjunto con los termodatos de las estaciones en terreno, permitieron establecer los parámetros que explican espacialmente el fenómeno de temperatura en el país y cómo ésta se comporta de acuerdo a las diferencias geográficas propias del territorio. A través del modelamiento geoestadístico, el conocimiento empírico y los ajustes teóricos, se estableció un Modelo de Regresión Lineal Múltiple, que estima las temperaturas de la superficie con alta fiabilidad. / Abstract. The growing scientific studies in geomatics allow understanding phenomena related to Earth system. Spatial modelling,remote sensing data, spatial information management and studies on planetary dynamism as tools are keys for ameliorating the knowledge on natural and human-derived phenomena. The advance on technology sets huge load information handle for studying the environmental variables as land surface temperature. Nevertheless, to obtain those data in the oldway involves economics costs and, certainly, an insufficient spatial cover. Because this information is required for large agronomic studies, Earth- Atmosphere mathematical models and climate change. Facing this necessity, the use for remote sensing data grows as much as objectives and goals in research. In Colombia, the low spatial cover by meteorology stations allow no-information places might be estimated using geomatics tools. In this research, Modis Land Surface Temperature –LST product with terrestrial stations data, allow establishing parameters that can explain land surface temperature phenomenon spatially in the country and how it behaves according to spatial geographic variances. Using geostatical modelling, statical processes and theorical basis, a Regression Model was stablished with high fidelity for estimating land surface temperatureMaestrí

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

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

Scale-Wavelength Decomposition of Hyperspectral Signals - Use for Mineral Classification & Quantification

An approach for material identification & soil constituent quantification based on a generalized multi-scale derivative analysis of hyperspectral signals is presented. It employs the continuous wavelet transform to project input spectra onto a scale-wavelength space. This allows investigating the spectra at selectable level of detail while normalizing/separating disturbances. Benefits & challenges of this decomposition for mineral classification & quantification will be shown for a mining site