Hyperspectral Imaging System Model Implementation and Analysis

In support of hyperspectral imaging system design and parameter trade-off research, an analytical end-to-end model to simulate the remote sensing system pipeline and to forecast remote sensing system performance has been implemented. It is also being made available to the remote sensing community through a website. Users are able to forecast hyperspectral imaging system performance by defining an observational scenario along with imaging system parameters. For system modeling, the implemented analytical model includes scene, sensor and target characteristics as well as atmospheric features, background spectral reflectance statistics, sensor specifications and target class reflectance statistics. The sensor model has been extended to include the airborne ProspecTIR instrument. To validate the analytical model, experiments were designed and conducted. The predictive system model has been verified by comparing the forecast results to ones obtained using real world data collected during the RIT SHARE 2012 collection. Results include the use of large calibration panels to show the predicted radiance consistent with the collected data. Grass radiance predicted from ground truth reflectance data also compare well with the real world collected data, and an eigenvector analysis also supports the validity of the predictions. Two examples of subpixel target detection scenario are presented. One is to detect subpixel wood yellow painted planks in an asphalt playground, and the other is to detect subpixel green painted wood planks in grass. To validate our system performance, the detection performance are analyzed using receiver operating characteristic (ROC) curves in a comprehensive scenario setting. The predicted ROC result of the yellow planks matches well the ROC derived from collected data. However, the predicted ROC curve of green planks differs from collected data ROC curve. Additional experiments were conducted and analyzed to discuss the possible reasons of the mismatch including scene characterization inaccuracy. Several subpixel target detection parameter trade-off analyses are given, including relative calibration error vs SNR, the relationship among probability of detection, meteorological range, pixel fill factor, relative calibration error and false alarm rate. These trade-off analyses explain the utility of this model for hyperspectral imaging system design and research

Multiscale Imaging of Evapotranspiration

Evapotranspiration (ET; evaporation + transpiration) is central to a wide range of biological, chemical, and physical processes in the Earth system. Accurate remote sensing of ET is challenging due to the interrelated and generally scale dependent nature of the physical factors which contribute to the process. The evaporation of water from porous media like sands and soils is an important subset of the complete ET problem. Chapter 1 presents a laboratory investigation into this question, examining the effects of grain size and composition on the evolution of drying sands. The effects of composition are found to be 2-5x greater than the effects of grain size, indicating that differences in heating caused by differences in reflectance may dominate hydrologic differences caused by grain size variation. In order to relate the results of Chapter 1 to the satellite image archive, however, the question of information loss between hyperspectral (measurements at 100s of wavelength intervals) laboratory measurements and multispectral (≤ 12 wavelength intervals) satellite images must be addressed. Chapter 2 focuses on this question as applied to substrate materials such as sediment, soil, rock, and non-photosynthetic vegetation. The results indicate that the continuum that is resolved by multispectral sensors is sufficient to resolve the gradient between sand-rich and clay-rich soils, and that this gradient is also a dominant feature in hyperspectral mixing spaces where the actual absorptions can be resolved. Multispectral measurements can be converted to biogeophysically relevant quantities using spectral mixture analysis (SMA). However, retrospective multitemporal analysis first requires cross-sensor calibration of the mixture model. Chapter 3 presents this calibration, allowing multispectral image data to be used interchangeably throughout the Landsat 4-8 archive. In addition, a theoretical explanation is advanced for the observed superior scaling properties of SMA-derived fraction images over spectral indices. The physical quantities estimated by the spectral mixture model are then compared to simultaneously imaged surface temperature, as well as to the derived parameters of ET Fraction and Moisture Availability. SMA-derived vegetation abundance is found to produce substantially more informative ET maps, and SMA-derived substrate fraction is found to yield a surprisingly strong linear relationship with surface temperature. These results provide context for agricultural applications. Chapter 5 investigates the question of mapping and monitoring rice agricultural using optical and thermal satellite image time series. Thermal image time series are found to produce more accurate maps of rice presence/absence, but optical image time series are found to produce more accurate maps of rice crop timing. Chapter 6 takes a more global approach, investigating the spatial structure of agricultural networks for a diverse set of landscapes. Surprisingly consistent scaling relations are found. These relations are assessed in the context of a network-based approach to land cover analysis, with potential implications for the scale dependence of ET estimates. In sum, this thesis present a novel approach to improving ET estimation based on a synthesis of complementary laboratory measurements, satellite image analysis, and field observations. Alone, each of these independent sources of information provides novel insights. Viewed together, these insights form the basis of a more accurate and complete geophysical understanding of the ET phenomenon

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

Stratified spectral mixture analysis of medium resolution imagery for impervious surface mapping

Linear spectral mixture analysis (LSMA) is widely employed in impervious surface estimation, especially for estimating impervious surface abundance in medium spatial resolution images. However, it suffers from a difficulty in endmember selection due to within-class spectral variability and the variation in the number and the type of endmember classes contained from pixel to pixel, which may lead to over or under estimation of impervious surface. Stratification is considered as a promising process to address the problem. This paper presents a stratified spectral mixture analysis in spectral domain (Sp_SSMA) for impervious surface mapping. It categorizes the entire data into three groups based on the Combinational Build-up Index (CBI), the intensity component in the color space and the Normalized Difference Vegetation Index (NDVI) values. A suitable endmember model is developed for each group to accommodate the spectral variation from group to group. The unmixing into the associated subset (or full set) of endmembers in each group can make the unmixing adaptive to the types of endmember classes that each pixel actually contains. Results indicate that the Sp_SSMA method achieves a better performance than full-set-endmember SMA and prior-knowledge-based spectral mixture analysis (PKSMA) in terms of R, RMSE and SE

Downscaling Landsat-8 land surface temperature maps in diverse urban landscapes using multivariate adaptive regression splines and very high resolution auxiliary data

We propose a method for spatial downscaling of Landsat 8-derived LST maps from 100(30 m) resolution down to 2–4 m with the use of the Multiple Adaptive Regression Splines (MARS) models coupled with very high resolution auxiliary data derived from hyperspectral aerial imagery and large-scale topographic maps. We applied the method to four Landsat 8 scenes, two collected in summer and two in winter, for three British towns collectively representing a variety of urban form. We used several spectral indices as well as fractional coverage of water and paved surfaces as LST predictors, and applied a novel method for the correction of temporal mismatch between spectral indices derived from aerial and satellite imagery captured at different dates, allowing for the application of the downscaling method for multiple dates without the need for repeating the aerial survey. Our results suggest that the method performed well for the summer dates, achieving RMSE of 1.40–1.83 K prior to and 0.76–1.21 K after correction for residuals. We conclude that the MARS models, by addressing the non-linear relationship of LST at coarse and fine spatial resolutions, can be successfully applied to produce high resolution LST maps suitable for studies of urban thermal environment at local scales

DETERMINING WHERE INDIVIDUAL VEHICLES SHOULD NOT DRIVE IN SEMIARID TERRAIN IN VIRGINIA CITY, NV

This thesis explored elements involved in determining and mapping where a vehicle should not drive off-road in semiarid areas. Obstacles are anything which slows or obstructs progress (Meyer et al., 1977) or limits the space available for maneuvering (Spenko et al., 2006). This study identified the major factors relevant in determining which terrain features should be considered obstacles when off-road driving and thus should be avoided. These are elements relating to the vehicle itself and how it is driven as well as terrain factors of slope, vegetation, water, and soil. Identification of these in the terrain was done using inferential methods of Terrain Pattern Recognition (TPR), analyzing of remotely sensing data, and Digital Elevation Map (DEM) data analysis. Analysis was further refined using other reference information about the area. Other factors such as weather, driving angle, and environmental impact are discussed. This information was applied to a section of Virginia City, Nevada as a case-study. Analysis and mapping was done purposely without field work prior to mapping to determine what could be assessed using only remote means. Not all findings from the literature review could be implemented in this trafficability study. Some methods and trafficability knowledge could not be implemented and were omitted due to data being unavailable, un-acquirable, or being too coarsely mapped to be useful. Examples of these are Lidar mapping of the area, soil profiling of the terrain, and assessment of plant species present in the area for driven-over traction and tire punctures. The Virginia City section was analyzed and mapped utilizing hyperspectral remotely sensed image data, remote-sensor-derived DEM data was used in a Geographical Information Systems (GIS). Stereo-paired air photos of the study site were used in TPR. Other information on flora, historical weather, and a previous soil survey map were used in a Geographical Information System (GIS). Field validation was used to check findings.The case study's trafficability assessment demonstrated methodologies of terrain analysis which successfully classified many materials present and identified major areas where a vehicle should not drive. The methods used were: Manual TPR of the stereo-paired air photo using a stereo photo viewer to conduct drainage-tracing and slope analysis of the DEM was done using automated methods in ArcMap. The SpecTIR hyperspectral data was analyzed using the manual Environment for Visualizing Images (ENVI) software hourglass procedure. Visual analysis of the hyperspectral data and air photos along with known soil and vegetation characteristics were used to refine analyses. Processed data was georectified using SpecTIR Geographic Lookup Table (GLT) input geometry, and exported to and analyzed in ArcMap with the other data previously listed. Features were identified based on their spectral attributes, spatial properties, and through visual analysis. Inaccuracies in mapping were attributable largely to spatial resolution of Digital Elevation Maps (DEMs) which averaged out some non-drivable obstacles and parts of a drivable road, subjective human and computer decisions during the processing of the data, and grouping of spectral end-members during hyperspectral data analysis. Further refinements to the mapping process could have been made if fieldwork was done during the mapping process.Mapping and field validation found: several manmade and natural obstacles were visible from the ground, but these obstacles were too fine, thin, or small to be identified from the remote sensing data. . Examples are fences and some natural terrain surface roughness - where the terrain's surface deviated from being a smooth surface, exhibiting micro- variations in surface elevation and/or textures. Slope analysis using the 10-meter and 30-meter resolution DEMs did not accurately depict some manmade features [eg. some of the buildings, portions of roads, and fences], evident with a well-trafficked paved road showing in DEM analysis as having too steep a slope [beyond 15°] to be drivable. Some features had been spectrally grouped together during analysis, due to similar spectral properties. Spectral grouping is a process where the spectral class's pixel areas are reviewed and classes which have too few occurrences are averaged into similar classes or dropped entirely. This is done to reduce the number of spectrally unique material classes to those that are most relevant to the terrain mapped. These decisions are subjective and in one case two similar spectral material classes were combined. In later evaluation should have remained as two separate material classes. In field sample collection, some of the determined features; free-standing water and liquid tanks, were found to be inaccessible due to being on private land and/or fence secured. These had to be visually verified - photos were also taken. Further refinements to the mapping could have been made if fieldwork was done during the mapping process. Determining and mapping where a vehicle should not drive in semiarid areas is a complex task which involves many variables and reference data types. Processing, analyzing, and fusing these different references entails subjective manual and automated decisions which are subject to errors and/or inaccuracies at multiple levels that can individually or collectively skew results, causing terrain trafficability to be depicted incorrectly. That said, a usable reference map is creatable which can assist decision makers when determining their route(s)

A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery

Semantic segmentation (classification) of Earth Observation imagery is a crucial task in remote sensing. This paper presents a comprehensive review of technical factors to consider when designing neural networks for this purpose. The review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and transformer models, discussing prominent design patterns for these ANN families and their implications for semantic segmentation. Common pre-processing techniques for ensuring optimal data preparation are also covered. These include methods for image normalization and chipping, as well as strategies for addressing data imbalance in training samples, and techniques for overcoming limited data, including augmentation techniques, transfer learning, and domain adaptation. By encompassing both the technical aspects of neural network design and the data-related considerations, this review provides researchers and practitioners with a comprehensive and up-to-date understanding of the factors involved in designing effective neural networks for semantic segmentation of Earth Observation imagery.Comment: 145 pages with 32 figure

The 2014-2015 lava flow field at Holuhraun: Deriving physical properties of the lava using multi remote sensing techniques and datasets

The purpose of this thesis is to employ remote sensing to study lava flow products during the 2014-2015 eruption at Holuhraun, Iceland. Multimodal remote sensing techniques and datasets were applied and developed for three study themes (1) deriving thermal properties from satellite infrared remote sensing, (2) differentiating lava surface using airborne hyperspectral remote sensing, and (3) quantifying lava surface roughness from elevation model acquired by airborne LiDAR. In the first study, we present a new approach based on infrared satellite images to derive thermal properties within the lava field during eruption and then compare the results with field measurement during the 2014-2015 eruption at Holuhraun. We develop a new spectral index for Landsat 8, named the thermal eruption index (TEI), based on the SWIR and TIR bands (bands 6 and 10). The purpose of the TEI consists mainly of two parts: (i) as a threshold for differentiating between different thermal domains; and (ii) to apply dualband technique to determine the maximum subpixel temperature (Th) of the lava. Lava surface roughness effects are accounted for by using the Hurst exponent (H), which is estimated from radar backscattering profiles. A higher H (smooth surface) generates thinner crust and high thermal flux meanwhile a lower H (rough surface) generates thicker crust and lower thermal flux. The total thermal flux peak is underestimated compared to other studies, although the trend shows good agreement with both field observation and other studies. In the second study, we focus on retrieving the lava surface types contributing to the signal recorded by airborne hyperspectral at the very top surface of the 2014-2015 lava flow field at Holuhraun. For this purpose, an airborne hyperspectral image acquired at Holuhraun with an AisaFENIX sensor onboard a NERC (Natural Environment Research Council Airborne Research Facility) campaign. For sub-pixel analysis, we used the sequential maximum angle convex cone (SMACC) algorithm to identify the spectral image endmembers and the linear spectral mixture analysis (LSMA) method was employed to retrieve the abundances. SMACC and LSMA methods offer a fast selection for volcanic product segregation. However, ground-truthing of spectra is recommended for future work. In the third study, we perform both the topographic position index (TPI) and onedimensional Hurst Exponent to derived lava flow unit roughness on the 2014-2015 lava flow field at Holuhraun using both airborne LiDAR and photogrammetry topography datasets. The roughness assessment was acquired from four lava flow features: (1) spiny pāhoehoe, (2) lava pond, (3) rubbly pāhoehoe lava, and (4) inflated channel. The TPI patterns on spiny lava and inflated channels show that the intermediate TPI values correspond to a small slope indicating a flat and smooth surface. Lava pond is characterized by low to high TPI values and forms a wave-like pattern. Meanwhile, irregular transitions patterns from low to high TPI values characterize lava with rough blocky surfaces, i.e. rubbly pāhoehoe to 'ā'a flows and lobes and their margins. These lobes and margins may give the impression of having similar roughness as the ”rough” surface on meters scale since this is an “apparent” roughness. On centimeters scale these multitudes of lobes feature coherent and smooth surfaces because they are pāhoehoe. The surface roughness of these lava features falls within the H range of 0.30 ± 0.05 to 0.76 ± 0.04. The rubbly pāhoehoe / 'ā'a has the roughest surface and the inflated lava channel along with pāhoehoe feature the smoothest surfaces among these four surface types. In general, the Hurst exponent values in the 2014-2015 lava field at Holuhraun has a strong tendency in 0.5, which is compatible with results from other study of geological surface roughness. Overall, this project provides an important insights into the application of remote sensing for monitoring and studying active lava flow fields and the techniques developed here will benefit such work in future events.Tilgangurinn með verkefninu var að rannsaka hraunrennsli og landform er urðu til í eldgosinu norðan Vatnajökuls 2014-2015 og kennt við Holuhraun. Fjölþátta fjarkönnunartækni og gögn úr gervitunglum og flugvélum voru nýtt við úrvinnslu verkefnisins. Rannsóknin sneri að þremur megin þáttum: (1) greiningu á eðli varmaútstreymis frá Holuhrauni, út frá innrauðri varmageislun sem mæld er með gervitunglagögnum (2) aðgreining á mismunandi hraunyfirborði, út frá ofur-fjölrófs mælingum úr lofti, og (3) greiningu og flokkun á yfirborðshrjúfleika Holuhrauns út frá hæðarlíkani er aflað var með LiDAR settur upp í flugvél. Fyrsti þáttur beindist að eðli varmaútstreymis á meðan á eldgosi stóð. Stuðst var við gervitunglagögn og mælingar með FLIR tækni á meðan eldgosið stóð yfir. Afraksturinn er nýr hitastuðull fyrir Landsat 8 og greiningu á eldgosum, (TEI). Hitastuðullinn TEI er unninn út frá SWIR og TIR böndum Landsat 8 (bönd 6 og 10). Með TEI næst fram tvennt: (i) að greina þröskuld milli tveggja hitasviða; og (ii) að beita tvíbanda tækni til að greina hitastig innan hverrar myndeiningar (Th) af hrauninu. Hrjúfleiki hraunsins hefur áhrif á varmaútstreymi, og er gert ráð fyrir honum með því að reikna Hurst veldisstuðulinn (H) og eru reiknuð út frá radar endurkasti hraunyfirborðs. Hátt H einkennir flatt og mjúkt yfirborð og þunna skorpu á hrauninu, á meðan að lágt H einkennir úfið yfirborð, þykka skorpu og lága varmaútgeislun. Heildar varmaútgeislun með þessari aðferð er heldur vanmetin en ofmetin í samanburði við aðrar aðferðir. Hinsvegar er góð fylgni með mælingum í mörkinni og samanburðar aðferðum. Annar hluti rannsóknarinnar sneri að túlkun ofur-fjölrófsgreininga á yfirborði Holuhrauns. Flogið var yfir Holuhraun sumarið 2015 með ofur-fjölrófsmæli (AisaFENIX) um borð í flugvél frá NERC (Natural Environment Research Council Airborne Research Facility). Við greiningu á yfirborði innan hverrar myndeiningar var, (i) notast við aðferð runubundins hámarkshorns kúptrar keilu (SMACC) til að finna útmörk ofurrófs mælinganna, (ii) blönduð línulega rófgreining (LSMA) var nýtt til að greina styrk eða gnægð innan myndeiningar. SMACC og LSMA aðferðirnar bjóða upp á mjög hraða greiningu á yfirborði og útfellingum efna á yfirborðið. Hins vegar þarf að gera fleiri rófmælingar á staðnum, til þess að auka notkunnargetu aðferðarinnar í hraungosum framtíðarinnar. Þriðji þáttur rannsóknarinnar sneri að því að greina landfræðilega stöðuvísitölu (TPI) og einvíðan Hurst veldisvísi til að meta hrjúfleika á hinu endanlega yfirborði Holuhrauns. Við þessa greiningu var notast við LiDAR mælingu af hrauninu og hæðagrunn unninn út frá ljósmyndum. Hrjúfleikinn var metinn fyrir fjögur yfirborð sem einkenna hraunið: (1) broddahraun „spiny pāhoehoe lava“, (2) hrauntjörn „lava pond“, (3) klumpahraun „rubbly pāhoehoe lava“ og (4) upptjakkaða hrauntröð „inflated lava channel“. TPI fyrir yfirborð (1) og (4) gefur meðalgildi sem einkennist af litlum halla og flötu yfirborði. Hrauntjörnin einkennist af lágum og háum TPI gildum sem endurspegla bylgjukennt mynstur. Hinsvegar einkennast hrjúfustu yfirborðin (3) og hraunjaðrar af óreglulegu mynstri lágra og hárra TPI gilda. Hrjúfleika stuðull þessara yfirborða H, er á bilinu 0.30 ± 0.05 til 0.76 ± 0.04. Mestur er hrjúfleiki kubbahrauna og minnstur er hrjúfleiki þandar hrauntraðar. Hurts veldisvísir Holuhrauns er nærri 0.5, en það er í mjög góðu samræmi við niðurstöður fyrri rannsókna á jarðfræðilegum yfirborðum. Í heild gefur verkefnið mikilvæga sýn á notagildi fjarkönnunaraðferða við rauntímaeftirlit með hraungosum, m.a. með þróun stuðla sem munu nýtast við atburði framtíðar. Þá voru tengsl hraunmyndana við ýmsa eiginleika eldgosa skýrð, sem aftur getur gefið vísbendingar um eðli fyrri atburða

Summaries of the Fifth Annual JPL Airborne Earth Science Workshop. Volume 1: AVIRIS Workshop

This publication is the first of three containing summaries for the Fifth Annual JPL Airborne Earth Science Workshop, held in Pasadena, California, on January 23-26, 1995. The main workshop is divided into three smaller workshops as follows: (1) The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) workshop, on January 23-24. The summaries for this workshop appear in this volume; (2) The Airborne Synthetic Aperture Radar (AIRSAR) workshop, on January 25-26. The summaries for this workshop appear in Volume 3; and (3) The Thermal Infrared Multispectral Scanner (TIMS) workshop, on January 26. The summaries for this workshop appear in Volume 2

Multispectral and Hyperspectral Remote Sensing Data for Mineral Exploration and Environmental Monitoring of Mined Areas

In recent decades, remote sensing technology has been incorporated in numerous mineral exploration projects in metallogenic provinces around the world. Multispectral and hyperspectral sensors play a significant role in affording unique data for mineral exploration and environmental hazard monitoring. This book covers the advances of remote sensing data processing algorithms in mineral exploration, and the technology can be used in monitoring and decision-making in relation to environmental mining hazard. This book presents state-of-the-art approaches on recent remote sensing and GIS-based mineral prospectivity modeling, offering excellent information to professional earth scientists, researchers, mineral exploration communities and mining companies