Enhancing the spatial resolution of satellite-derived land surface temperature mapping for urban areas

Land surface temperature (LST) is an important environmental variable for urban studies such as those focused on the urban heat island (UHI). Though satellite-derived LST could be a useful complement to traditional LST data sources, the spatial resolution of the thermal sensors limits the utility of remotely sensed thermal data. Here, a thermal sharpening technique is proposed which could enhance the spatial resolution of satellite-derived LST based on super-resolution mapping (SRM) and super-resolution reconstruction (SRR). This method overcomes the limitation of traditional thermal image sharpeners that require fine spatial resolution images for resolution enhancement. Furthermore, environmental studies such as UHI modelling typically use statistical methods which require the input variables to be independent, which means the input LST and other indices should be uncorrelated. The proposed Super-Resolution Thermal Sharpener (SRTS) does not rely on any surface index, ensuring the independence of the derived LST to be as independent as possible from the other variables that UHI modelling often requires. To validate the SRTS, its performance is compared against that of four popular thermal sharpeners: the thermal sharpening algorithm (TsHARP), adjusted stratified stepwise regression method (Stepwise), pixel block intensity modulation (PBIM), and emissivity modulation (EM). The privilege of using the combination of SRR and SRM was also verified by comparing the accuracy of SRTS with sharpening process only based on SRM or SRR. The results show that the SRTS can enhance the spatial resolution of LST with a magnitude of accuracy that is equal or even superior to other thermal sharpeners, even without requiring fine spatial resolution input. This shows the potential of SRTS for application in conditions where only limited meteorological data sources are available yet where fine spatial resolution LST is desirable

Automatic Features Extraction From Time Series Of Passive Microwave Images For Snowmelt Detection Using Deep-Learning – A Bidirectional Long-Short Term Memory Autoencoder (Bi-Lstm-Ae) Approach.

The Antarctic surface snowmelt is prone to the polar climate and is common in its coastal regions. With about 90 percent of the planet\u27s glaciers, if all of the Antarctica glaciers melted, sea levels will rise about 58 meters around the planet. The development of an effective automated ice-sheet snowmelt monitoring system is therefore crucial. Microwave remote sensing instruments, on the one hand, are very sensitive to snowmelt and can see day and night through clouds, allowing us to distinguish melting from dry snow and to better understand when, where, and for how long melting has taken place. On the other hand, deep-learning (DL) algorithms, which can learn from linear and non-linear data in a hierarchical way robust representations and discriminative features, have recently become a hotspot in the field of machine learning and have been implemented with success in the geospatial and remote sensing field. This study demonstrates that deep learning, particularly long-short memory autoencoder architecture (LSTM-AE) is capable of fully exploiting archives of passive microwave time series data. In this thesis, An LSTM-AE algorithm was used to reduce and capture essential relationships between attributes stored as brightness temperature within pixel time series and k-means clustering is applied to cluster the leaned representations. The final output map highlights the melt extent in Antarctica

Empirical Measurement and Model Validation of Infrared Spectra of Contaminated Surfaces

The goal of this thesis was to validate predicted infrared spectra of liquid contaminated surfaces from a micro-scale bi-directional reflectance distribution function (BRDF) model through the use of empirical measurement. Liquid contaminated surfaces generally require more sophisticated radiometric modeling to numerically describe surface properties. The Digital Image and Remote Sensing Image Generation (DIRSIG) model utilizes radiative transfer modeling to generate synthetic imagery for a variety of applications. Aside from DIRSIG, a micro-scale model known as microDIRSIG has been developed as a rigorous ray tracing physics-based model that could predict the BRDF of geometric surfaces that are defined as micron to millimeter resolution facets. The model offers an extension from the conventional BRDF models by allowing contaminants to be added as geometric objects to a micro-facet surface. This model was validated through the use of Fourier transform infrared spectrometer measurements. A total of 18 different substrate and contaminant combinations were measured and compared against modeled outputs. The substrates used in this experiment were wood and aluminum that contained three different paint finishes. The paint finishes included no paint, Krylon ultra-flat black, and Krylon glossy black. A silicon based oil (SF96) was measured out and applied to each surface to create three different contamination cases for each surface. Radiance in the longwave infrared region of the electromagnetic spectrum was measured by a Design and Prototypes (D\&P) Fourier transform infrared spectrometer and a Physical Sciences Inc. Adaptive Infrared Imaging Spectroradiometer (AIRIS). The model outputs were compared against the measurements quantitatively in both the emissivity and radiance domains. A temperature emissivity separation (TES) algorithm had to be applied to the measured radiance spectra for comparison with the microDIRSIG predicted emissivity spectra. The model predicted emissivity spectra was also forward modeled through a DIRSIG simulation for comparisons to the radiance measurements. The results showed a promising agreement for homogeneous surfaces with liquid contamination that could be well characterized geometrically. Limitations arose in substrates that were modeled as homogeneous surfaces, but had spatially varying artifacts due to uncertainties with contaminant and surface interactions. There is high desire for accurate physics based modeling of liquid contaminated surfaces and this validation framework may be extended to include a wider array of samples for more realistic natural surfaces that are often found in real world scenarios

Site Characterization Using Integrated Imaging Analysis Methods on Satellite Data of the Islamabad, Pakistan, Region

We develop an integrated digital imaging analysis approach to produce a first-approximation site characterization map for Islamabad, Pakistan, based on remote-sensing data. We apply both pixel-based and object-oriented digital imaging analysis methods to characterize detailed (1:50,000) geomorphology and geology from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery. We use stereo-correlated relative digital elevation models (rDEMs) derived from ASTER data, as well as spectra in the visible near-infrared (VNIR) to thermal infrared (TIR) domains. The resulting geomorphic units in the study area are classified as mountain (including the Margala Hills and the Khairi Murat Ridge), piedmont, and basin terrain units. The local geologic units are classified as limestone in the Margala Hills and the Khairi Murat Ridge and sandstone rock types for the piedmonts and basins. Shear-wave velocities for these units are assigned in ranges based on established correlations in California. These ranges include Vs30-values to be greater than 500 m/sec for mountain units, 200–600 m/sec for piedmont units, and less than 300 m/sec for basin units. While the resulting map provides the basis for incorporating site response in an assessment of seismic hazard for Islamabad, it also demonstrates the potential use of remote-sensing data for site characterization in regions where only limited conventional mapping has been done

Thermal error modelling of machine tools based on ANFIS with fuzzy c-means clustering using a thermal imaging camera

Thermal errors are often quoted as being the largest contributor to CNC machine tool errors, but they can be effectively reduced using error compensation. The performance of a thermal error compensation system depends on the accuracy and robustness of the thermal error model and the quality of the inputs to the model. The location of temperature measurement must provide a representative measurement of the change in temperature that will affect the machine structure. The number of sensors and their locations are not always intuitive and the time required to identify the optimal locations is often prohibitive, resulting in compromise and poor results. In this paper, a new intelligent compensation system for reducing thermal errors of machine tools using data obtained from a thermal imaging camera is introduced. Different groups of key temperature points were identified from thermal images using a novel schema based on a Grey model GM (0, N) and Fuzzy c-means (FCM) clustering method. An Adaptive Neuro-Fuzzy Inference System with Fuzzy c-means clustering (FCM-ANFIS) was employed to design the thermal prediction model. In order to optimise the approach, a parametric study was carried out by changing the number of inputs and number of membership functions to the FCM-ANFIS model, and comparing the relative robustness of the designs. According to the results, the FCM-ANFIS model with four inputs and six membership functions achieves the best performance in terms of the accuracy of its predictive ability. The residual value of the model is smaller than ± 2 μm, which represents a 95% reduction in the thermally-induced error on the machine. Finally, the proposed method is shown to compare favourably against an Artificial Neural Network (ANN) model

Automated Synthetic Scene Generation

First principles, physics-based models help organizations developing new remote sensing instruments anticipate sensor performance by enabling the ability to create synthetic imagery for proposed sensor before a sensor is built. One of the largest challenges in modeling realistic synthetic imagery, however, is generating the spectrally attributed, three-dimensional scenes on which the models are based in a timely and affordable fashion. Additionally, manual and semi-automated approaches to synthetic scene construction which rely on spectral libraries may not adequately capture the spectral variability of real-world sites especially when the libraries consist of measurements made in other locations or in a lab. This dissertation presents a method to fully automate the generation of synthetic scenes when coincident lidar, Hyperspectral Imagery (HSI), and high-resolution imagery of a real-world site are available. The method, called the Lidar/HSI Direct (LHD) method, greatly reduces the time and manpower needed to generate a synthetic scene while also matching the modeled scene as closely as possible to a real-world site both spatially and spectrally. Furthermore, the LHD method enables the generation of synthetic scenes over sites in which ground access is not available providing the potential for improved military mission planning and increased ability to fuse information from multiple modalities and look angles. The LHD method quickly and accurately generates three-dimensional scenes providing the community with a tool to expand the library of synthetic scenes and therefore expand the potential applications of physics-based synthetic imagery modeling

US Office of Naval Research, Solid Mechanics Program Review

The purpose of this extended abstract is to provide an overview of activities relating to performance assessments. The work described is wide ranging and not intended to provide a detailed account of any particular approach