Multispectral Images

tAverage maize yield in eastern Africa is 2.03 t ha−1as compared to global average of 6.06 t ha−1due tobiotic and abiotic constraints. Amongst the biotic production constraints in Africa, stem borers are themost injurious. In eastern Africa, maize yield losses due to stem borers are currently estimated between12% and 21% of the total production. The objective of the present study was to explore the possibility ofRapidEye spectral data to assess stem borer larva densities in maize fields in two study sites in Kenya.RapidEye images were acquired for the Bomet (western Kenya) test site on the 9th of December 2014and on 27th of January 2015, and for Machakos (eastern Kenya) a RapidEye image was acquired on the3rd of January 2015. Five RapidEye spectral bands as well as 30 spectral vegetation indices (SVIs) wereutilized to predict per field maize stem borer larva densities using generalized linear models (GLMs),assuming Poisson (‘Po’) and negative binomial (‘NB’) distributions. Root mean square error (RMSE) andratio prediction to deviation (RPD) statistics were used to assess the models performance using a leave-one-out cross-validation approach. The Zero-inflated NB (‘ZINB’) models outperformed the ‘NB’ modelsand stem borer larva densities could only be predicted during the mid growing season in Decemberand early January in both study sites, respectively (RMSE = 0.69–1.06 and RPD = 8.25–19.57). Overall, allmodels performed similar when all the 30 SVIs (non-nested) and only the significant (nested) SVIs wereused. The models developed could improve decision making regarding controlling maize stem borerswithin integrated pest management (IPM) interventions.

The objective of this research was to develop an off-line artificial vision system to automatically detect defective eggshells, i.e., dirty or cracked eggshells, by employing multispectral images with the final purpose of adapting the system to an on-line grading machine. In particular, this work was focused on studying the feasibility of identifying organic stains on brown eggshells (dirty eggshell) caused by blood, feathers, feces, etc., from natural stains caused by deposits of pigments on the outer layer of clean eggshells. During the analysis, a total of 384 eggs were evaluated (clean, 148; dirty, 236). Dirty samples were evaluated visually in order to classify them according to the kind of defect (blood, feathers, and white, clear or dark feces), and clean eggshells were classified on the basis of the color of the natural stains (clear or dark). For each sample, digital images were acquired by employing a charged coupled device camera endowed with 15 monochromatic filters (440–940 nm). A Matlab® function was developed in order to automate the process and analyze images with the aim to classify samples as clean or dirty. The program was constituted by three major steps: first, the research of an opportune combination of monochromatic images in order to isolate the eggshell from the background; second, the detection of the dirt stains; third, the classification of the image samples into the dirty or clean group on the basis of the geometric characteristics of the stains (area in pixel). The proposed classification algorithm was able to correctly classify nearly 98% of the samples with a very low processing time (0.05 s). The robustness of the proposed classification was observed applying an external validation to a second set of samples (n = 178), obtaining a similar percentage of correctly classified samples (97%).

The IKONOS satellite simultaneously collects 1-m panchromatic and 4-m multispectral images, providing the commercial and scientific community with a dramatic improvement in spatial resolution over previously available satellite imagery. The sun-synchronous IKONOS orbit ...

The focus of this study is to use a multi-analytical approach to map and identify the pigments used on a polychrome wooden coffin (Late Period), as well as to provide a deeper understanding of the painting techniques and the condition of the object. Moreover, the authors are significantly interested in the identification of the wood species. These were identified by observing the thin sections of samples under an optical transmission light microscope; the painted materials layered on the wood surface were analyzed by several scientific and analytical methods. Furthermore, the previous consolidation material and salts were also identified. The application of technical photography provided useful information about the spatial distribution of the surviving original pigments and the materials used in previous restoration interventions, in particular visible-induced luminescence, which played an important role to recognize spatial distribution of areas containing Egyptian blue, even if it is in traces or mixed with other pigments. However complete characterization of the pigments was only possible with the use of other techniques. Resumo Este estudo pretende usar uma abordagem multi-analítica para mapear e identificar os pigmentos usados num sarcófago de madeira policromada (Época Baixa), assim como proporcionar um mais detalhado conhecimento das técnicas de pintura e do seu estado de conservação. Além disso, os autores estão especialmente interessados na identificação das espécies de madeira. Estas foram identificadas através da observação, ao microscópio óptico de transmissão, de lâminas delgadas e a camada cromática sobre a madeira foi analisada com o recurso a diferentes métodos de exame e análise. Além disso foram igualmente identificados o material de consolidação utilizado numa intervenção anterior e os sais. A fotografia técnica proporcionou informação útil sobre a distri-buição espacial dos pigmentos originais e dos materiais usados em anteriores intervenções. A lu-minescência visível induzida foi sobremaneira útil para se perceber a distribuição do azul egípcio, mesmo quando em concentrações vestigiais ou misturado com outros pigmentos. No entanto, a caracterização detalhada dos pigmentos só foi possível através dos outros métodos usados.

Among the many manuscripts at the Freer Gallery of Art is a lavishly illuminated copy of the Gulistan of Sa‘di (F1998.5). This manuscript was transcribed in an elegant nasta‘liq script by renowned calligrapher Sultan ‘Ali Mashhadi in Herāt (present-day Afghanistan) in 1468, but much of its history is unknown. The text includes six paintings that were added in the seventeenth century during the reign of Mughal emperor Shah Jahan in India. Stains on the versos of the painted pages provide tantalizing traces of the existence of earlier illustrations underneath the Mughal ones. A technical study incorporating infrared and ultraviolet imaging, X-ray computed radiography, and targeted pigment analyses has revealed new information about these preexisting paintings. The size of the figures and the intermediate changes to the compositions suggest there were several working phases for the manuscript. A revised chronology now includes these phases and further enriches our understanding of this complex manuscript. From a technical standpoint, this research highlights both the challenges and undeniable potential of imaging technology for the study of Islamic manuscript paintings, many of which have been reworked at various times in their history.

The problem of recursively approximating motion resulting from the Optical Flow (OF) in video thru Total Least Squares (TLS) techniques is addressed. TLS method solves an inconsistent system Gu=z , with G and z in error due to temporal/spatial derivatives, and nonlinearity, while the Ordinary Least Squares (OLS) model has noise only in z. Sources of difficulty involve the non-stationarity of the field, the ill-posedness, and the existence of noise in the data. Three ways of applying the TLS with different noise conjectures to the end problem are observed. First, the classical TLS (cTLS) is introduced, where the entries of the error matrices of each row of the augmented matrix [G;z] have zero mean and the same standard deviation. Next, the Generalized Total Least Squares (GTLS) is defined to provide a more stable solution, but it still has some problems. The Generalized Scaled TLS (GSTLS) has G and z tainted by different sources of additive zero-mean Gaussian noise and scaling [G;z] by nonsingular D and E, that is, D[G;z]E makes the errors iid with zero mean and a diagonal covariance matrix. The scaling is computed from some knowledge on the error distribution to improve the GTLS estimate. For moderate levels of additive noise, GSTLS outperforms the OLS, and the GTLS approaches. Although any TLS variant requires more computations than the OLS, it is still applicable with proper scaling of the data matrix.

Digital imaging technology can now produce detailed and trustworthy surrogates of historical documents. Leveraged by technological improvements in imaging and image processing, humanities scholars have been able to image, analyse, and recover more information from historical documents than was previously possible. Multispectral imaging has been utilised to examine the characteristics of documents by providing additional information about their physical properties and condition. However, current digitisation efforts have concentrated on recording documents in their current state. In this project we used multispectral imaging to record the process of macroscopic document degradation by imaging a parchment document before and after a series of degradation processes. This project is part of a larger effort to investigate the methodologies for acquiring and processing multispectral images of damaged cultural heritage documents.

Improved sensor characteristics are generally assumed to increase the potential accuracy of image classification and information extraction from remote sensing imagery. However, the increase in data volume caused by these improvements raise challenges associated with the selection, storage, and processing of this data, and with the cost-effective and timely analysis of the remote sensing datasets. Previous research has extensively assessed the relevance and impact of spatial, spectral and temporal resolution of satellite data on classification accuracy, but little attention has been given to the impact of radiometric resolution. This study focuses on the role of radiometric resolution on classification accuracy of remote sensing data through different classification experiments over three different sites. The experiments were carried out using fine and low scale radiometric resolution images classified through a bagging classification tree. The classification experiments addressed different aspects of the classification road map, including among others, binary and multiclass classification schemes, spectrally and spatially enhanced images, as well as pixel and objects as units of the classification. In addition, the impact of image radiometric resolution on computational time and the information content in fine- and low-resolution images was also explored. While in certain cases, higher radiometric resolution has led to up to 8% higher classification accuracies compared to lower resolution radiometric data, other results indicate that higher radiometric resolution does not necessarily imply improved classification accuracy. Also, classification accuracy of spectral indices and texture bands is not related so much to the radiometric resolution of the original remote sensing images but rather to their own radiometric resolution. Overall, the results of this study suggest that data selection and classification need not always adhere to the highest possible radiometric resolution.

A spectral imaging system comprising a 39-Mpixel monochrome camera, LED-based narrowband illumination, and acquisition/control software has been designed for investigations of cultural heritage objects. Notable attributes of this system, referred to as EurekaVision, include: streamlined workflow, flexibility, provision of well-structured data and metadata for downstream processing, and illumination that is safer for the artifacts. The system design builds upon experience gained while imaging the Archimedes Palimpsest and has been used in studies of a number of important objects in the LOC collection. This paper describes practical issues that were considered by EurekaVision to address key research questions for the study of fragile and unique cultural objects over a range of spectral bands. The system is intended to capture important digital records for access by researchers, professionals, and the public. The system was first used for spectral imaging of the 1507 world map by Martin Waldseemueller, the first printed map to reference "America." It was also used to image sections of the Carta Marina 1516 map by the same cartographer for comparative purposes. An updated version of the system is now being utilized by the Preservation Research and Testing Division of the Library of Congress.

In this paper, we have proposed an approach for building extraction from very high resolution (VHR) multispectral images using NDVI (Normalized Difference Vegetation Index) based segmentation and morphological operations. This approach uses both spatial and spectral properties of an image scene for building detection. Spectral properties are related to NDVI based segmentation and spatial properties are related to the morphological

Classical methods for classification of pixels in multispectral images include supervised classifiers such as
the maximum-likelihood classifier, neural network classifiers, fuzzy neural networks, support vector
machines, and decision trees. Recently, there has been an increase of interest in ensemble learning – a
method that generates many classifiers and aggregates their results. Breiman proposed Random Forestin
2001 for classification and clustering. Random Forest grows many decision trees for classification. To
classify a new object, the input vector is run through each decision tree in the forest. Each tree gives a
classification. The forest chooses the classification having the most votes. Random Forest provides a robust
algorithm for classifying large datasets. The potential of Random Forest is not been explored in analyzing
multispectral satellite images. To evaluate the performance of Random Forest, we classified multispectral
images using various classifiers such as the maximum likelihood classifier, neural network, support vector
machine (SVM), and Random Forest and compare their results.

Resumo: Os recentes avanços na tecnologia de sensoriamento remoto proporcionaram o desenvolvimento de sensores orbitais com diferentes características de resoluções e de potenciais em discriminar componentes da matéria, inclusive presentes na água. Atualmente, ainda não foi muito explorado a aplicação de imagens de sensores orbitais multiespectrais de média resolução espacial e espectral, em estudos da qualidade d?água, principalmente de ambientes aquáticos lóticos. A avaliação de componentes opticamente ativos (COAs) por sensoriamento remoto, pode ser continuamente aplicada para o monitoramento da qualidade da água, auxiliando no diagnóstico das condições ambientais de rios e lagos, e dar subsídios para a gestão destes. Diante disso, o objetivo geral deste trabalho foi avaliar a contribuição das imagens do satélite Landsat 8/OLI na detecção de COAs em ambientes lóticos e semilênticos do rio Tibagi, por meio da coleta de dados limnológicos atuais ?in situ?, e da análise espectral orbital, buscando a construção de modelos empíricos para inferência da concentração e distribuição de COAs. Foram selecionadas duas áreas no rio Tibagi: a represa da UHE Mauá, e proximidades do ponto de captação d?agua da Sanepar. O rio Tibagi é uma importante fonte de abastecimento de água, nasce nos municípios de Palmeira e Ponta Grossa, no centro do Estado do Paraná, e tem sua foz na margem esquerda do rio Paranapanema, na divisa com o Estado de São Paulo (SUDERHSA, 1992). A metodologia comtempla a aquisição dos dados limnológicos simultaneamente à aquisição das imagens Landsat 8/OLI, e posterior correlação dos dados. Os modelos empíricos foram gerados a partir de regressão linear múltipla no software R. A pesquisa apresenta resultados de modelos empíricos para a estimativa de clorofila-a, total de sólidos em suspensão e turbidez, com aplicações em imagens Landsat 8/OLI. Os resultados obtidos em ambientes aquáticos lóticos apontam um potencial para estudos que empreguem imagens orbitais multiespectrais de alta resolução espacial, ou hiperespectrais para a detecção de COAs.

Abstract: Recent advances in remote sensing technology have led to the development of orbital sensors with different characteristics of resolution and potential to discriminate components of matter, including in water. Currently, has not been much explored the application of multispectral images from orbital sensors with medium spectral and spatial resolution in studies of water quality, mainly in lotic aquatic environments. The evaluation of optically active constituents by remote sensing can be continuously applied to the monitoring of water quality, and help in the diagnosis of the environmental conditions of rivers and lakes, and to provide subsidies for their management. Therefore, the objective of this work was to evaluate the contribution of Landsat 8/OLI satellite imagery in the detection of optically active constituents in lotic and semilentic environments of the Tibagi River, by collecting current limnological data "in situ", and by orbital spectral analysis, seeking the construction of empirical models for inference of concentration and distribution of optically active constituents. To do so, two areas were selected on the Tibagi River: the dam of Mauá Hydroelectric Power Plant, and near the water catchment point of Sanepar. The Tibagi river is an important source of water supply, born in the municipalities of Palmeira and Ponta Grossa, in the center of the State of Paraná, and its mouth on the left bank of the Paranapanema River, on the border with the State of São Paulo (SUDERHSA, 1992). The methodology contemplates the acquisition of limnological data simultaneously to the acquisition of Landsat 8/OLI images, and subsequent correlation of the data. Empirical models were generated by multiple linear regression in software R. The research presents results of empirical models for the estimation of Chlorophyll-a, Total Solids in Suspension and Turbidity, with applications in Landsat 8/OLI images. The results obtained in lotic aquatic environments show a pointer to studies that use multispectral orbital images of high spatial resolution, or hyperspectral for the detection of optically active constituents.

Multispectral imaging cameras are frequently used in art conservation for identifying pigments, as well as for monitoring change in pigments over time. Multispectral cameras take multiple images at 370nm, 448nm, 476nm, 499nm, 519nm, 598nm, 636nm, 700nm, 735nm, 780nm, 870nm, and 940nm wavelengths with UV bandpass, visible bandpass, and long pass filters to increase the range of captured information to include UV reflectance and fluorescence emission images. This pilot study explores the ability to utilize this non-destructive technique to analyze variability of pigment recipes within ceramic types and between types. Mineral and organic black pigments from five black-on-white ceramic types (Jemez, Cortez, Chuska, Red Mesa, and Abiquiu) from the American Southwest were used for comparative purposes. Pigment and reflectance values acquired from images via Image J software (open source freeware) were compared both within types and between types with one-way ANOVA analysis. Results show varying levels of significant statistical difference between types and minimal difference within most types sampled.

Hlavním cílem zde prezentovaného projektu bylo srovnání výsledků dlouhodobého letecko-archeologického průzkumu, resp. šikmých leteckých fotografií pořízených z nízko letícího letadla nad tzv. starým sídelním územím Čech, s výpovědí družicových snímků s velmi ...