Development of a spectral unmixing procedure using a genetic algorithm and spectral shape

xvi, 85 leaves : ill. (chiefly col.) ; 29 cmSpectral unmixing produces spatial abundance maps of endmembers or ‘pure’ materials using sub-pixel scale decomposition. It is particularly well suited to extracting a greater portion of the rich information content in hyperspectral data in support of real-world issues such as mineral exploration, resource management, agriculture and food security, pollution detection, and climate change. However, illumination or shading effects, signature variability, and the noise are problematic. The Least Square (LS) based spectral unmixing technique such as Non-Negative Sum Less or Equal to One (NNSLO) depends on “shade” endmembers to deal with the amplitude errors. Furthermore, the LS-based method does not consider amplitude errors in abundance constraint calculations, thus, often leads to abundance errors. The Spectral Angle Constraint (SAC) reduces the amplitude errors, but the abundance errors remain because of using fully constrained condition. In this study, a Genetic Algorithm (GA) was adapted to resolve these issues using a series of iterative computations based on the Darwinian strategy of ‘survival of the fittest’ to improve the accuracy of abundance estimates. The developed GA uses a Spectral Angle Mapper (SAM) based fitness function to calculate abundances by satisfying a SAC-based weakly constrained condition. This was validated using two hyperspectral data sets: (i) a simulated hyperspectral dataset with embedded noise and illumination effects and (ii) AVIRIS data acquired over Cuprite, Nevada, USA. Results showed that the new GA-based unmixing method improved the abundance estimation accuracies and was less sensitive to illumination effects and noise compared to existing spectral unmixing methods, such as the SAC and NNSLO. In case of synthetic data, the GA increased the average index of agreement between true and estimated abundances by 19.83% and 30.10% compared to the SAC and the NNSLO, respectively. Furthermore, in case of real data, GA improved the overall accuracy by 43.1% and 9.4% compared to the SAC and NNSLO, respectively

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas

Machine Learning Approaches for Natural Resource Data

Abstract Real life applications involving efficient management of natural resources are dependent on accurate geographical information. This information is usually obtained by manual on-site data collection, via automatic remote sensing methods, or by the mixture of the two. Natural resource management, besides accurate data collection, also requires detailed analysis of this data, which in the era of data flood can be a cumbersome process. With the rising trend in both computational power and storage capacity, together with lowering hardware prices, data-driven decision analysis has an ever greater role. In this thesis, we examine the predictability of terrain trafficability conditions and forest attributes by using a machine learning approach with geographic information system data. Quantitative measures on the prediction performance of terrain conditions using natural resource data sets are given through five distinct research areas located around Finland. Furthermore, the estimation capability of key forest attributes is inspected with a multitude of modeling and feature selection techniques. The research results provide empirical evidence on whether the used natural resource data is sufficiently accurate enough for practical applications, or if further refinement on the data is needed. The results are important especially to forest industry since even slight improvements to the natural resource data sets utilized in practice can result in high saves in terms of operation time and costs. Model evaluation is also addressed in this thesis by proposing a novel method for estimating the prediction performance of spatial models. Classical model goodness of fit measures usually rely on the assumption of independently and identically distributed data samples, a characteristic which normally is not true in the case of spatial data sets. Spatio-temporal data sets contain an intrinsic property called spatial autocorrelation, which is partly responsible for breaking these assumptions. The proposed cross validation based evaluation method provides model performance estimation where optimistic bias due to spatial autocorrelation is decreased by partitioning the data sets in a suitable way. Keywords: Open natural resource data, machine learning, model evaluationTiivistelmä Käytännön sovellukset, joihin sisältyy luonnonvarojen hallintaa ovat riippuvaisia tarkasta paikkatietoaineistosta. Tämä paikkatietoaineisto kerätään usein manuaalisesti paikan päällä, automaattisilla kaukokartoitusmenetelmillä tai kahden edellisen yhdistelmällä. Luonnonvarojen hallinta vaatii tarkan aineiston keräämisen lisäksi myös sen yksityiskohtaisen analysoinnin, joka tietotulvan aikakautena voi olla vaativa prosessi. Nousevan laskentatehon, tallennustilan sekä alenevien laitteistohintojen myötä datapohjainen päätöksenteko on yhä suuremmassa roolissa. Tämä väitöskirja tutkii maaston kuljettavuuden ja metsäpiirteiden ennustettavuutta käyttäen koneoppimismenetelmiä paikkatietoaineistojen kanssa. Maaston kuljettavuuden ennustamista mitataan kvantitatiivisesti käyttäen kaukokartoitusaineistoa viideltä eri tutkimusalueelta ympäri Suomea. Tarkastelemme lisäksi tärkeimpien metsäpiirteiden ennustettavuutta monilla eri mallintamistekniikoilla ja piirteiden valinnalla. Väitöstyön tulokset tarjoavat empiiristä todistusaineistoa siitä, onko käytetty luonnonvaraaineisto riittävän laadukas käytettäväksi käytännön sovelluksissa vai ei. Tutkimustulokset ovat tärkeitä erityisesti metsäteollisuudelle, koska pienetkin parannukset luonnonvara-aineistoihin käytännön sovelluksissa voivat johtaa suuriin säästöihin niin operaatioiden ajankäyttöön kuin kuluihin. Tässä työssä otetaan kantaa myös mallin evaluointiin esittämällä uuden menetelmän spatiaalisten mallien ennustuskyvyn estimointiin. Klassiset mallinvalintakriteerit nojaavat yleensä riippumattomien ja identtisesti jakautuneiden datanäytteiden oletukseen, joka ei useimmiten pidä paikkaansa spatiaalisilla datajoukoilla. Spatio-temporaaliset datajoukot sisältävät luontaisen ominaisuuden, jota kutsutaan spatiaaliseksi autokorrelaatioksi. Tämä ominaisuus on osittain vastuussa näiden oletusten rikkomisesta. Esitetty ristiinvalidointiin perustuva evaluointimenetelmä tarjoaa mallin ennustuskyvyn mitan, missä spatiaalisen autokorrelaation vaikutusta vähennetään jakamalla datajoukot sopivalla tavalla. Avainsanat: Avoin luonnonvara-aineisto, koneoppiminen, mallin evaluoint

Estimating landscape irrigated areas and potential water conservation at the rural-urban interface using remote sensing and GIS

Research goals were to analyze patterns of urban landscape water use, assess landscape water conservation potential, and identify locations with capacity to conserve. Methodological contributions involved acquiring airborne multispectral digital images over two urban cities which were processed, classified, and imported into a GIS environment where landscaped area were extracted and combined with property and water billing data and local evapotranspiration rates to calculate landscape irrigation applications exceeding estimated water needs. Additional analyses were conducted to compare classified aerial images to ground-measured landscaped areas, landscaped areas to total parcel size, water use on residential and commercial properties, and turf areas under tress when they were leafed out and bare. Results verified the accuracy and value of this approach for municipal water management, showed more commercial properties applied water in excess of estimated needs compared to residential ones, and that small percentages of users accounted for most of the excess irrigatio

Hierarchical Nanostructure of Natural Biominerals and Man-made Semiconductors

Materials with structural hierarchy have become a central focus to inspire new designs of next-generation high-performance materials. Using 3D hierarchical architectures that traverse the atomic, nano-, micro-, to macro-scale with precision, nature and humans exploit exotic physical properties or better performance beyond the inherent properties of the materials, such as diffracting iridescence of nacre, unique quantum effects, and parallel computing. However, visible light is a demarcation point because conventional microscopy such as optical microscope cannot resolve the materials below this length scale. In this thesis, we apply scanning transmission electron microscopy (STEM) to investigate materials down to angstrom length scales using the recent advancement of aberration-corrected electromagnetic lenses. First half of this work provides systematic approach on Nacre to understand the superior toughness, the mesocrystalline order, and the self-correcting growth. The second half of this work provides experimental approach on Group III-Nitrides to understand the structure and chemistry attributable to enhance solar conversion efficiency. The first chapter motivates materials characterization by high-energy electrons for natural biominerals and man-made semiconductors. The exceptional resolving power of STEM with spectroscopic techniques are able to reveal the structural behavior of nacre from macro- to nanoscale and the exotic new phases in group III-nitride at atomic scale. In Chapter II, our investigation of nacre deformation reveals the underlying nanomechanics that govern the structural resilience and absorption of mechanical energy1. Using high-resolution S/TEM combined with in-situ indentation, we observe nanoscale recovery of heavily deformed nacre. The combination of soft nanoscale organic components with inorganic nanograins hierarchically designed by natural organisms results in highly ductile structural materials that can withstand mechanical impact and exhibit high resilience on the macro- and nano-scale. Chapter III presents Nacre’s remarkable medium-range mesocrystal formed through corrective processes that remedy disorder and topological defects2. In layered growth of nanomaterials, external guidelines don’t exist and mesocrystallinity is prohibitive. In rare instances Nature unconsciously assembles mesocrystals—which merits our attention. The entire nanostructure of nacreous pearls is characterized in cross-section to reveal complex stochastic processes that govern ordered nacre growth. Mollusks strike balance between preserving translational symmetry and reducing thickness variation by creating a paracrystal with medium-range order (5.5 µm). This balance allows Pearls to attenuate the initial disorder during early formation and maintain order throughout a changing external environment. In Chapter IV, the thesis extends the InGaN ternary system, that is an optimal photoelectrode for efficient solar hydrogen production3-5. However, it is difficult to grow high crystalline InGaN with uniformly homogeneous indium composition because In-rich crystals are highly strained causing phase segregation and subsequent performance degradation6. Here, aberration-corrected STEM combined with analytic spectroscopy such as EELS and XEDS is used to study crystallinity and compositional uniformity in 1D InGaN heteroepitaxy. Finally, in Chapter V we discuss AlGaN ternary system for high-efficiency deep UV light sources. It is the only alternative technology to replace mercury lamps for water purification and disinfection7-9. At present, however, AlGaN-based mid- and deep UV LEDs exhibit very low efficiency. Here, we investigate the interface phenomenon of 2D AlGaN such as tunnel junction, quantum wall, and nanoclusters in active region to enhance light emitting performance9-12.PHDMaterials Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/167958/1/gjiseok_1.pd