Signature Simulation and Characterization of Mixed Solids in the Visible and Thermal Regimes

Solid target signatures vary due to geometry, chemical composition and scene radiometry. Although radiative transfer models and function-fit physical models may describe certain targets in limited depth, the ability to incorporate all three of these signature variables is difficult. This work describes a method to simulate the transient signatures of mixed solids and soils by first considering scene geometry that was synthetically created using 3-d physics engines. Through the assignment of spectral data from the Nonconventional Exploitation Factors Data System (NEFDS) and other libraries, synthetic scenes are represented as a chemical mixture of particles. Finally, first principles radiometry is modeled using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model. With DIRSIG, radiometric and sensing conditions were systematically manipulated to produce goniometric signatures. The implementation of this virtual goniometer allows users to examine how a target bidirectional reflectance function (BRDF) and directional emissivity will change with geometry, composition and illumination direction. The tool described provides geometry flexibility that is unmatched by radiative transfer models. It delivers a discrete method to avoid the significant cost of time and treasure associated with hardware based goniometric data collections

OSIrIS: a physically based simulation tool to improve training in thermal infrared remote sensing over urban areas at high spatial resolution

International audienceThis paper describes an infrared image simulator for remote sensing applications, called OSIrIS (outdoor scene and infrared image simulation). It has been developed partly for training and reproduces with great details the physical phenomena that play a major role in complex urban environment. OSIrIS performs a synthesis of scene based on a 3-D description of the landscape with a high spatial resolution (0.5 – 10 m). The physical processes are briefly described and their importance with respect to the objectives are discussed. Thermal emission depends on temperature and generally dominates the signal. Temperature is governed by heat equation and is solved by the means of boundary conditions such as in-depth temperature and flux balance at surface. Main parameters are solar and atmospheric radiations, wind, heat conduction and changes in humidity. An innovative approach was developed to take into account variations in time of the interactions between the landscape and the physical processes. OSIrIS aims at simulating situations that are encountered in reality. It enables users self-formation, helping them understanding changes in image radiance as a function of the input parameters and their own simulation requirements. Examples are given that illustrate specific aspects of infrared images

Harmonization of space-borne infra-red sensors measuring sea surface temperature

Sea surface temperature (SST) is observed by a constellation of sensors, and SST retrievals are commonly combined into gridded SST analyses and climate data records (CDRs). Differential biases between SSTs from different sensors cause errors in such products, including feature artefacts. We introduce a new method for reducing differential biases across the SST constellation, by reconciling the brightness temperature (BT) calibration and SST retrieval parameters between sensors. We use the Advanced Along-Track Scanning Radiometer (AATSR) and the Sea and Land Surface Temperature Radiometer (SLSTR) as reference sensors, and the Advanced Very High Resolution Radiometer (AVHRR) of the MetOp-A mission to bridge the gap between these references. Observations across a range of AVHRR zenith angles are matched with dual-view three-channel skin SST retrievals from the AATSR and SLSTR. These skin SSTs act as the harmonization reference for AVHRR retrievals by optimal estimation (OE). Parameters for the harmonized AVHRR OE are iteratively determined, including BT bias corrections and observation error covariance matrices as functions of water-vapor path. The OE SSTs obtained from AVHRR are shown to be closely consistent with the reference sensor SSTs. Independent validation against drifting buoy SSTs shows that the AVHRR OE retrieval is stable across the reference-sensor gap. We discuss that this method is suitable to improve consistency across the whole constellation of SST sensors. The approach will help stabilize and reduce errors in future SST CDRs, as well as having application to other domains of remote sensing

Radiation techniques for urban thermal simulation with the Finite Element Method

Modern societies are increasingly organized in cities. In the present times, more than half of the world’s population lives in urban settlements. In this context, architectural and building scale works have the need of extending their scope to the urban environment. One of the main challenges of these times is understanting all the thermal exchanges that happen in the city. The radiative part appears as the less developed one; its characterization and interaction with built structures has gained attention for building physics, architecture and environmental engineering. Providing a linkage between these areas, the emerging field of urban physics has become important for tackling studies of such nature. Urban thermal studies are intrinsically linked to multidisciplinary work approaches. Performing full-scale measurements is hard, and prototype models are difficult to develop. Therefore, computational simulations are essential in order to understand how the city behaves and to evaluate projected modifications. The methodological and algorithmic improvement of simulation is one of the mainlines of work for computational physics and many areas of computer science. The field of computer graphics has addressed the adaptation of rendering algorithms to daylighting using physically-based radiation models on architectural scenes. The Finite Element Method (FEM) has been widely used for thermal analysis. The maturity achieved by FEM software allows for treating very large models with a high geometrical detail and complexity. However, computing radiation exchanges in this context implies a hard computational challenge, and forces to push the limits of existing physical models. Computer graphics techniques can be adapted to FEM to estimate solar loads. In the thermal radiation range, the memory requirements for storing the interaction between the elements grows because all the urban surfaces become radiation sources. In this thesis, a FEM-based methodology for urban thermal analysis is presented. A set of radiation techniques (both for solar and thermal radiation) are developed and integrated into the FEM software Cast3m. Radiosity and ray tracing are used as the main algorithms for radiation computations. Several studies are performed for different city scenes. The FEM simulation results are com-pared with measured temperature results obtained by means of urban thermography. Post-processing techniques are used to obtain rendered thermograms, showing that the proposed methodology pro-duces accurate results for the cases analyzed. Moreover, its good computational performance allows for performing this kind of study using regular desktop PCs.Las sociedades modernas están cada vez más organizadas en ciudades. Más de la mitad de la población mundial vive en asentamientos urbanos en la actualidad. En este contexto, los trabajos a escala arquitectónica y de edificio deben extender su alcance al ambiente urbano. Uno de los mayores desafíos de estos tiempos consiste en entender todos los intercambios térmicos que suceden en la ciudad. La parte radiativa es la menos desarrollada; su caracterización y su interacción con edificaciones ha ganado la atención de la física de edificios, la arquitectura y la ingeniería ambiental. Como herramienta de conexión entre estas áreas, la física urbana es un área que resulta importante para atacar estudios de tal naturaleza. Los estudios térmicos urbanos están intrinsecamente asociados a trabajos multidisciplinarios. Llevar a cabo mediciones a escala real resulta difícil, y el desarrollo de prototipos de menor escala es complejo. Por lo tanto, la simulación computacional es esencial para entender el comportamiento de la ciudad y para evaluar modificaciones proyectadas. La mejora metodológica y algorítmica de las simulaciones es una de las mayores líneas de trabajo para la física computacional y muchas áreas de las ciencias de la computación. El área de la computación gráfica ha abordado la adaptación de algoritmos de rendering para cómputo de iluminación natural, utilizando modelos de radiación basados en la física y aplicándolos sobre escenas arquitectónicas. El Método de Elementos Finitos (MEF) ha sido ampliamente utilizado para análisis térmico. La madurez alcanzada por soluciones de software MEF permite tratar grandes modelos con un alto nivel de detalle y complejidad geométrica. Sin embargo, el cómputo del intercambio radiativo en este contexto implica un desafío computacional, y obliga a empujar los límites de las descripciones físicas conocidas. Algunas técnicas de computación gráfica pueden ser adaptadas a MEF para estimar las cargas solares. En el espectro de radiación térmica, los requisitos de memoria necesarios para almacenar la interacción entre los elementos crecen debido a que todas las superficies urbanas se transforman en fuentes emisoras de radiación. En esta tesis se presenta una metodología basada en MEF para el análisis térmico de escenas urbanas. Un conjunto de técnicas de radiación (para radiación solar y térmica) son desarrolladas e integradas en el software MEF Cast3m. Los algoritmos de radiosidad y ray tracing son utilizados para el cómputo radiativo. Se presentan varios estudios que utilizan diferentes modelos de ciudades. Los resultados obtenidos mediante MEF son comparados con temperaturas medidas por medio de termografías urbanas. Se utilizan técnicas de post-procesamiento para renderizar imágenes térmicas, que permiten concluir que la metodología propuesta produce resultados precisos para los casos analizados. Asimismo, su buen desempeño computacional posibilita realizar este tipo de estudios en computadoras personales

A Novel Building Temperature Simulation Approach Driven by Expanding Semantic Segmentation Training Datasets with Synthetic Aerial Thermal Images

Multi-sensor imagery data has been used by researchers for the image semantic segmentation of buildings and outdoor scenes. Due to multi-sensor data hunger, researchers have implemented many simulation approaches to create synthetic datasets, and they have also synthesized thermal images because such thermal information can potentially improve segmentation accuracy. However, current approaches are mostly based on the laws of physics and are limited to geometric models’ level of detail (LOD), which describes the overall planning or modeling state. Another issue in current physics-based approaches is that thermal images cannot be aligned to RGB images because the configurations of a virtual camera used for rendering thermal images are difficult to synchronize with the configurations of a real camera used for capturing RGB images, which is important for segmentation. In this study, we propose an image translation approach to directly convert RGB images to simulated thermal images for expanding segmentation datasets. We aim to investigate the benefits of using an image translation approach for generating synthetic aerial thermal images and compare those approaches with physics-based approaches. Our datasets for generating thermal images are from a city center and a university campus in Karlsruhe, Germany. We found that using the generating model established by the city center to generate thermal images for campus datasets performed better than using the latter to generate thermal images for the former. We also found that using a generating model established by one building style to generate thermal images for datasets with the same building styles performed well. Therefore, we suggest using training datasets with richer and more diverse building architectural information, more complex envelope structures, and similar building styles to testing datasets for an image translation approach