Subpixel temperature estimation from single-band thermal infrared imagery

Target temperature estimation from thermal infrared (TIR) imagery is a complex task that becomes increasingly more difficult as the target size approaches the size of a projected pixel. At that point the assumption of pixel homogeneity is invalid as the radiance value recorded at the sensor is the result of energy contributions from the target material and any other background material that falls within a pixel boundary. More often than not, thermal infrared pixels are heterogeneous and therefore subpixel temperature extraction becomes an important capability. Typical subpixel estimation approaches make use of multispectral or hyperspectral sensors. These technologies are expensive and multispectral or hyperspectral thermal imagery might not be readily available for a target of interest. A methodology was developed to retrieve the temperature of an object that is smaller than a projected pixel of a single-band TIR image using physics-based modeling. Physics-based refers to the utilization of the Multi-Service Electro-optic Signature (MuSES) heat transfer model, the MODerate spectral resolution atmospheric TRANsmission (MODTRAN) atmospheric propagation algorithm, and the Digital Imaging and Remote Sensing Image Generation (DIRSIG) synthetic image generation model to reproduce a collected thermal image under a number of user-supplied conditions. A target space is created and searched to determine the temperature of the subpixel target of interest from a collected TIR image. The methodology was tested by applying it to single-band thermal imagery collected during an airborne campaign. The emissivity of the targets of interest ranged from 0.02 to 0.91 and the temperature extraction error for the high emissivity targets were similar to the temperature extraction errors found in published papers that employed multi-band techniques

Estimation of the instantaneous downward surface shortwave radiation using MODIS data in Lhasa for all-sky conditions

Measuring the solar irradiance with high accuracy is the basis of PV power forecasting. Although the downward surface shortwave radiation (DSSR) data derived from satellite images are widely used in the PV industry, the instantaneity and accuracy of these data are not suitable for PV power forecasting in a short-time period. In this study, an algorithm to calculate instantaneous DSSR for all-sky conditions was developed by combining clear-sky radiative transfer model and 3D radiative transfer model using MODIS products (MOD03-07, 09). The algorithm was evaluated by ground measurements from a station in Lhasa and a reference dataset from FLASHFlux. The results indicate that the errors of DSSR using combining model are less than FLASHFlux. The time consuming of running 3D radiative transfer model can be reduced by narrowing down the extent of input data to 8km

Anomaly detection in hyperspectral signatures using automated derivative spectroscopy methods

The goal of this research was to detect anomalies in remotely sensed Hyperspectral images using automated derivative based methods. A database of Hyperspectral signatures was used that had simulated additive Gaussian anomalies that modeled a weakly concentrated aerosol in several spectral bands. The automated pattern detection system was carried out in four steps. They were: (1) feature extraction, (2) feature reduction through linear discriminant analysis, (3) performance characterization through receiver operating characteristic curves, and (4) signature classification using nearest mean and maximum likelihood classifiers. The Hyperspectral database contained signatures with various anomaly concentrations ranging from weakly present to moderately present and also anomalies in various spectral reflective and absorptive bands. It was found that the automated derivative based detection system gave classification accuracies of 97 percent for a Gaussian anomaly of SNR -45 dB and 70 percent for Gaussian anomaly of SNR -85 dB. This demonstrates the applicability of using derivative analysis methods for pattern detection and classification with remotely sensed Hyperspectral images

High-Resolution Satellite Imagery Classification for Urban Form Detection

Mapping urban form at regional and local scales is a crucial task for discerning the influence of urban expansion upon the ecosystem and the surrounding environment. Remotely sensed imagery is ideally used to monitor and detect urban areas that occur frequently as a consequence of incessant urbanization. It is a lengthy process to convert satellite imagery into urban form map using the existing methods of manual interpretation and parametric image classification digitally. In this work, classification techniques of high-resolution satellite imagery were used to map 50 selected cities of study of the National Urban System in Mexico, during 2015–2016. In order to process the information, 140 RapidEye Ortho Tile multispectral satellite imageries with a pixel size of 5 m were downloaded, divided into 5 × 5 km tiles and then 639 tiles were generated. In each (imagery or tile), classification methods were tested, such as: artificial neural networks (RNA), support vector machines (MSV), decision trees (AD), and maximum likelihood (MV); after tests, urban and nonurban categories were obtained. The result is validated with an accuracy method that follows a stratified random sampling of 16 points for each tile. It is expected that these results can be used in the construction of spatial metrics that explain the differences in the Mexican urban areas

New Simulation and Fusion Techniques for Assessing and Enhancing UAS Topographic and Bathymetric Point cloud Accuracy

Imagery acquired from unmanned aircraft systems (UAS) and processed with structure from motion (SfM) – multi-view stereo (MVS) algorithms provides transformative new capabilities for surveying and mapping. Together, these new tools are leading to a democratization of airborne surveying and mapping by enabling similar capabilities (including similar or better accuracies, albeit from substantially lower altitudes) at a fraction of the cost and size of conventional aircraft. While SfM-MVS processing is becoming widely used for mapping topography, and more recently bathymetry, empirical accuracy assessments—especially, those aimed at investigating the sensitivity of point cloud accuracy to varying acquisition and processing parameters—can be difficult, expensive, and logistically complicated. Additional challenges in bathymetric mapping from UAS imagery using SfM-MVS software relate to refraction-induced errors and lack of coverage in areas of homogeneous sandy substrate. This dissertation aims to address these challenges through development and testing of new algorithms for SfM-MVS accuracy assessment and bathymetry retrieval. A new tool for simulating UAS imagery, simUAS, is presented and used to assess SfM-MVS accuracy for topographic mapping (Chapter 2) and bathymetric mapping (Chapter 3). The importance of simUAS is that it can be used to precisely vary one parameter at a time, while perfectly fixing all others, which is possible, because the UAS data are synthetically generated. Hence, the issues of uncontrolled variables, such as changing illumination levels and moving objects in the scene, which occur in empirical experiments using real UAS, are eliminated. Furthermore, simulated experiments using this approach can be performed without the need for costly and time-intensive fieldwork. The results of these studies demonstrate how processing settings and initial camera position accuracy relate to the accuracy of the resultant point cloud. For bathymetric processing, it was found that camera position accuracy is particularly important for generating accurate results. Even when accurate camera positions are acquired for bathymetric data, SfM-MVS processing is still unable to resolve depths in regions which lack seafloor texture, such as sandy, homogeneous substrate. A new methodology is introduced and tested which uses the results from the SfM-MVS processing to train a radiometric model, which estimates water depth based on the wavelength-dependent attenuation of light in the water column (Chapter 4). The methodology is shown to increase the spatial coverage and improve the accuracy of the bathymetric data at a field site on Buck Island off of St. Croix in the U.S. Virgin Islands. Collectively, this work is anticipated to facilitate greater use of UAS for nearshore bathymetric mapping

The Combined Use of Optical and SAR Data for Large Area Impervious Surface Mapping

One of the megatrends marking our societies today is the rapid growth of urban agglomerations which is accompanied by a continuous increase of impervious surface (IS) cover. In light of this, accurate measurement of urban IS cover as an indicator for both, urban growth and environmental quality is essential for a wide range of urban ecosystems studies. The aim of this work is to present an approach based on both optical and SAR data in order to quantify urban impervious surface as a continuous variable on regional scales. The method starts with the identification of relevant areas by a semi automated detection of settlement areas on the basis of single-polarized TerraSAR-X data. Thereby the distinct texture and the high density of dihedral corner reflectors prevailing in build-up areas are utilized to automatically delineate settlement areas by the use of an object-based image classification method. The settlement footprints then serve as reference area for the impervious surface estimation based on a Support Vector Regression (SVR) model which relates percent IS to spectral reflectance values. The training procedure is based on IS values derived from high resolution QuickBird data. The developed method is applied to SPOT HRG data from 2005 and 2009 covering almost the whole are of Can Tho Province in the Mekong Delta, Vietnam. In addition, a change detection analysis was applied in order to test the suitability of the modelled IS results for the automated detection of constructional developments within urban environments. Overall accuracies between 84 % and 91% for the derived settlement footprints and absolute mean errors below 15% for the predicted versus training percent IS values prove the suitability of the approach for an area-wide mapping of impervious surfaces thereby exclusively focusing on settlement areas on the basis of remotely sensed image data

Summaries of the Third Annual JPL Airborne Geoscience Workshop. Volume 1: AVIRIS Workshop

This publication contains the preliminary agenda and summaries for the Third Annual JPL Airborne Geoscience Workshop, held at the Jet Propulsion Laboratory, Pasadena, California, on 1-5 June 1992. This main workshop is divided into three smaller workshops as follows: (1) the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) workshop, on June 1 and 2; (2) the Thermal Infrared Multispectral Scanner (TIMS) workshop, on June 3; and (3) the Airborne Synthetic Aperture Radar (AIRSAR) workshop, on June 4 and 5. The summaries are contained in Volumes 1, 2, and 3, respectively

Modeling Spatial Surface Energy Fluxes of Agricultural and Riparian Vegetation Using Remote Sensing

Modeling of surface energy fluxes and evapotranspiration (ET) requires the understanding of the interaction between land and atmosphere as well as the appropriate representation of the associated spatial and temporal variability and heterogeneity. This dissertation provides new methodology showing how to rationally and properly incorporate surface features characteristics/properties, including the leaf area index, fraction of cover, vegetation height, and temperature, using different representations as well as identify the related effects on energy balance flux estimates including ET. The main research objectives were addressed in Chapters 2 through 4 with each presented in a separate paper format with Chapter 1 presenting an introduction and Chapter 5 providing summary and recommendations. Chapter 2 discusses a new approach of incorporating temporal and spatial variability of surface features. We coupled a remote sensing-based energy balance model with a traditional water balance method to provide improved estimates of ET. This approach was tested over rainfed agricultural fields ~ 10 km by 30 km in Ames, Iowa. Before coupling, we modified the water balance method by incorporating a remote sensing-based estimate for one of its parameters to ameliorate its performance on a spatial basis. Promising results were obtained with indications of improved estimates of ET and soil moisture in the root zone. The effects of surface features heterogeneity on measurements of turbulence were investigated in Chapter 3. Scintillometer-based measurements/estimates of sensible heat flux (H) were obtained over the riparian zone of the Cibola National Wildlife Refuge (CNWR), California. Surface roughness including canopy height (hc), roughness length, and zero-plane displacement height were incorporated in different ways, to improve estimates of H. High resolution, 1-m maps of ground surface digital elevation model and canopy height, hc, were derived from airborne LiDAR sensor data to support the analysis. The effects of using different pixel resolutions to account for surface feature variability on modeling energy fluxes, e.g., net radiation, soil, sensible, and latent heat, were studied in Chapter 4. Two different modeling approaches were applied to estimate energy fluxes and ET using high and low pixel resolution datasets obtained from airborne and Landsat sensors, respectively, provided over the riparian zone of the CNWR, California. Enhanced LiDAR-based hc maps were also used to support the modeling process. The related effects were described relative to leaf area index, fraction of cover, hc, soil moisture status at root zone, groundwater table level, and vegetation stress conditions

Imaging Sensors and Applications

In past decades, various sensor technologies have been used in all areas of our lives, thus improving our quality of life. In particular, imaging sensors have been widely applied in the development of various imaging approaches such as optical imaging, ultrasound imaging, X-ray imaging, and nuclear imaging, and contributed to achieve high sensitivity, miniaturization, and real-time imaging. These advanced image sensing technologies play an important role not only in the medical field but also in the industrial field. This Special Issue covers broad topics on imaging sensors and applications. The scope range of imaging sensors can be extended to novel imaging sensors and diverse imaging systems, including hardware and software advancements. Additionally, biomedical and nondestructive sensing applications are welcome