A Hierarchical Framework for the Classification of Multispectral Imagery

AbstractOut of the abundant digital image data available, multispectral imagery is one which gives us information about the earth we live in. To gain knowledge from multispectral imagery, it is essential to classify the data present in the image based on spectral information. Classification plays a significant role in understanding the remotely sensed data obtained from the satellites. This paper brings out a new classification scheme based on a hierarchical framework. The hierarchical model proposed in this paper helps to understand the imagery at different levels of abstractness and concreteness to serve different applications like town planning, facility management and so on. The model depicts classification of the multispectral imagery on three abstract levels. The algorithm proposed outputs classification at different levels with an average accuracy of 72.6% in level 1 and 78.3% in level 2. The time sensitivity analysis of the algorithm shows that it outperforms the traditional SVM classifier. A detailed analysis of the algorithm proposed is detailed in this paper with respect to the parameters influencing the classification accuracy

Early-season crop mapping using improved artificial immune network (IAIN) and Sentinel data

Substantial efforts have been made to identify crop types by region, but few studies have been able to classify crops in early season, particularly in regions with heterogeneous cropping patterns. This is because image time series with both high spatial and temporal resolution contain a number of irregular time series, which cannot be identified by most existing classifiers. In this study, we firstly proposed an improved artificial immune network (IAIN), and tried to identify major crops in Hengshui, China at early season using IAIN classifier and short image time series. A time series of 15-day composited images was generated from 10 m spatial resolution Sentinel-1 and Sentinel-2 data. Near-infrared (NIR) band and normalized difference vegetation index (NDVI) were selected as optimal bands by pair-wise Jeffries–Matusita distances and Gini importance scores calculated from the random forest algorithm. When using IAIN to identify irregular time series, overall accuracy of winter wheat and summer crops were 99% and 98.55%, respectively. We then used the IAIN classifier and NIR and NDVI time series to identify major crops in the study region. Results showed that winter wheat could be identified 20 days before harvest, as both the producer’s accuracy (PA) and user’s accuracy (UA) values were higher than 95% when an April 1–May 15 time series was used. The PA and UA of cotton and spring maize were higher than 95% with image time series longer than April 1–August 15. As spring maize and cotton mature in late August and September–October, respectively, these two crops can be accurately mapped 4–6 weeks before harvest. In addition, summer maize could be accurately identified after August 15, more than one month before harvest. This study shows the potential of IAIN classifier for dealing with irregular time series and Sentinel-1 and Sentinel-2 image time series at early-season crop type mapping, which is useful for crop management

Quantifying spatiotemporal changes of the urban impervious surface of Dhaka District using Remote sensing Technology

Dhaka, the capital of Bangladesh, is one of the world's fastest-growing cities where imperviousness expanding in tandem. Therefore, accurate estimation of impervious surfaces is essential for urban planning and management. This paper attempts to quantify the changes of urban impervious surfaces in Dhaka district from 1990 to 2020 using remote sensing technology. Satellite images of 1990, 1995, 2000, 2005, 2010, 2015, and 2020 have been taken from the Landsat TM, ETM+, OLI sensor. Unsupervised classification with k-means clustering and three different RS indices NDVI, NDBI, and BUI was used to delineate the actual impervious area of Dhaka city. This study reveals that due to urbanization a net increase of 67.30 sq. miles impervious area is added to the existing amount over the study period. In 2020 total 300.749 sq. miles which contain 51.02% of the total land were occupied by impervious surfaces compared to the 233.446 sq. miles in 1990. Instantaneously taking appropriate strategies is crucial for sustainable urban growth.  

Machine vision detection of pests, diseases, and weeds: A review

Most of mankind’s living and workspace have been or going to be blended with smart technologies like the Internet of Things. The industrial domain has embraced automation technology, but agriculture automation is still in its infancy since the espousal has high investment costs and little commercialization of innovative technologies due to reliability issues. Machine vision is a potential technique for surveillance of crop health which can pinpoint the geolocation of crop stress in the field. Early statistics on crop health can hasten prevention strategies such as pesticide, fungicide applications to reduce the pollution impact on water, soil, and air ecosystems. This paper condenses the proposed machine vision relate research literature in agriculture to date to explore various pests, diseases, and weeds detection mechanisms

Caracterização e estudo comparativo de exsudações de hidrocarbonetos e plays petrolíferos em bacias terrestres das regiões central do Irã e sudeste do Brasil usando sensoriamento remoto espectral

Orientador: Carlos Roberto de Souza FilhoTese (doutorado) - Universidade Estadual de Campinas, Instituto de GeociênciasResumo: O objetivo desta pesquisa foi explorar as assinaturas de exsudações de hidrocarbonetos na superfície usando a tecnologia de detecção remota espectral. Isso foi alcançado primeiro, realizando uma revisão abrangente das capacidades e potenciais técnicas de detecção direta e indireta. Em seguida, a técnica foi aplicada para investigar dois locais de teste localizados no Irã e no Brasil, conhecidos por hospedar sistemas ativos de micro-exsudações e afloramentos betuminosos, respectivamente. A primeira área de estudo está localizada perto da cidade de Qom (Irã), e está inserida no campo petrolífero Alborz, enterrado sob sedimentos datados do Oligoceno da Formação Upper Red. O segundo local está localizado perto da cidade de Anhembi (SP), na margem oriental da bacia do Paraná, no Brasil, e inclui acumulações de betume em arenitos triássicos da Formação Pirambóia. O trabalho na área de Qom integrou evidências de (i) estudos petrográficos e geoquímicos em laboratório, (ii) investigações de afloramentos em campo, e (iii) mapeamento de anomalia em larga escala através de conjuntos de dados multi-espectrais ASTER e Sentinel-2. O resultado deste estudo se trata de novos indicadores mineralógicos e geoquímicos para a exploração de micro-exsudações e um modelo de micro-exsudações atualizado. Durante este trabalho, conseguimos desenvolver novas metodologias para análise de dados espectroscópicos. Através da utilização de dados simulados, indicamos que o instrumento de satélite WorldView-3 tem potencial para detecção direta de hidrocarbonetos. Na sequência do estudo, dados reais sobre afloramentos de arenitos e óleo na área de Anhembi foram investigados. A área foi fotografada novamente no chão e usando o sistema de imagem hiperespectral AisaFENIX. Seguiu-se estudos e amostragem no campo,incluindo espectroscopia de alcance fechado das amostras no laboratório usando instrumentos de imagem (ou seja, sisuCHEMA) e não-imagem (ou seja, FieldSpec-4). O estudo demonstrou que uma abordagem espectroscópica multi-escala poderia fornecer uma imagem completa das variações no conteúdo e composição do betume e minerais de alteração que acompanham. A assinatura de hidrocarbonetos, especialmente a centrada em 2300 nm, mostrou-se consistente e comparável entre as escalas e capaz de estimar o teor de betume de areias de petróleo em todas as escalas de imagemAbstract: The objective of this research was to explore for the signatures of seeping hydrocarbons on the surface using spectral remote sensing technology. It was achieved firstly by conducting a comprehensive review of the capacities and potentials of the technique for direct and indirect seepage detection. Next, the technique was applied to investigate two distinctive test sites located in Iran and Brazil known to retain active microseepage systems and bituminous outcrops, respectively. The first study area is located near the city of Qom in Iran, and consists of Alborz oilfield buried under Oligocene sediments of the Upper-Red Formation. The second site is located near the town of Anhembi on the eastern edge of the Paraná Basin in Brazil and includes bitumen accumulations in the Triassic sandstones of the Pirambóia Formation. Our work in Qom area integrated evidence from (i) petrographic, spectroscopic, and geochemical studies in the laboratory, (ii) outcrop investigations in the field, and (iii) broad-scale anomaly mapping via orbital remote sensing data. The outcomes of this study was novel mineralogical and geochemical indicators for microseepage characterization and a classification scheme for the microseepage-induced alterations. Our study indicated that active microseepage systems occur in large parts of the lithofacies in Qom area, implying that the extent of the petroleum reservoir is much larger than previously thought. During this work, we also developed new methodologies for spectroscopic data analysis and processing. On the other side, by using simulated data, we indicated that WorldView-3 satellite instrument has the potential for direct hydrocarbon detection. Following this demonstration, real datasets were acquired over oil-sand outcrops of the Anhembi area. The area was further imaged on the ground and from the air by using an AisaFENIX hyperspectral imaging system. This was followed by outcrop studies and sampling in the field and close-range spectroscopy in the laboratory using both imaging (i.e. sisuCHEMA) and nonimaging instruments. The study demonstrated that a multi-scale spectroscopic approach could provide a complete picture of the variations in the content and composition of bitumen and associated alteration mineralogy. The oil signature, especially the one centered at 2300 nm, was shown to be consistent and comparable among scales, and capable of estimating the bitumen content of oil-sands at all imaging scalesDoutoradoGeologia e Recursos NaturaisDoutor em Geociências2015/06663-7FAPES

Hyperspectral Imaging from Ground Based Mobile Platforms and Applications in Precision Agriculture

This thesis focuses on the use of line scanning hyperspectral sensors on mobile ground based platforms and applying them to agricultural applications. First this work deals with the geometric and radiometric calibration and correction of acquired hyperspectral data. When operating at low altitudes, changing lighting conditions are common and inevitable, complicating the retrieval of a surface's reflectance, which is solely a function of its physical structure and chemical composition. Therefore, this thesis contributes the evaluation of an approach to compensate for changes in illumination and obtain reflectance that is less labour intensive than traditional empirical methods. Convenient field protocols are produced that only require a representative set of illumination and reflectance spectral samples. In addition, a method for determining a line scanning camera's rigid 6 degree of freedom (DOF) offset and uncertainty with respect to a navigation system is developed, enabling accurate georegistration and sensor fusion. The thesis then applies the data captured from the platform to two different agricultural applications. The first is a self-supervised weed detection framework that allows training of a per-pixel classifier using hyperspectral data without manual labelling. The experiments support the effectiveness of the framework, rivalling classifiers trained on hand labelled training data. Then the thesis demonstrates the mapping of mango maturity using hyperspectral data on an orchard wide scale using efficient image scanning techniques, which is a world first result. A novel classification, regression and mapping pipeline is proposed to generate per tree mango maturity averages. The results confirm that maturity prediction in mango orchards is possible in natural daylight using a hyperspectral camera, despite complex micro-illumination-climates under the canopy

Mineral identification using data-mining in hyperspectral infrared imagery

Les applications de l’imagerie infrarouge dans le domaine de la géologie sont principalement des applications hyperspectrales. Elles permettent entre autre l’identification minérale, la cartographie, ainsi que l’estimation de la portée. Le plus souvent, ces acquisitions sont réalisées in-situ soit à l’aide de capteurs aéroportés, soit à l’aide de dispositifs portatifs. La découverte de minéraux indicateurs a permis d’améliorer grandement l’exploration minérale. Ceci est en partie dû à l’utilisation d’instruments portatifs. Dans ce contexte le développement de systèmes automatisés permettrait d’augmenter à la fois la qualité de l’exploration et la précision de la détection des indicateurs. C’est dans ce cadre que s’inscrit le travail mené dans ce doctorat. Le sujet consistait en l’utilisation de méthodes d’apprentissage automatique appliquées à l’analyse (au traitement) d’images hyperspectrales prises dans les longueurs d’onde infrarouge. L’objectif recherché étant l’identification de grains minéraux de petites tailles utilisés comme indicateurs minéral -ogiques. Une application potentielle de cette recherche serait le développement d’un outil logiciel d’assistance pour l’analyse des échantillons lors de l’exploration minérale. Les expériences ont été menées en laboratoire dans la gamme relative à l’infrarouge thermique (Long Wave InfraRed, LWIR) de 7.7m à 11.8 m. Ces essais ont permis de proposer une méthode pour calculer l’annulation du continuum. La méthode utilisée lors de ces essais utilise la factorisation matricielle non négative (NMF). En utlisant une factorisation du premier ordre on peut déduire le rayonnement de pénétration, lequel peut ensuite être comparé et analysé par rapport à d’autres méthodes plus communes. L’analyse des résultats spectraux en comparaison avec plusieurs bibliothèques existantes de données a permis de mettre en évidence la suppression du continuum. Les expérience ayant menés à ce résultat ont été conduites en utilisant une plaque Infragold ainsi qu’un objectif macro LWIR. L’identification automatique de grains de différents matériaux tels que la pyrope, l’olivine et le quartz a commencé. Lors d’une phase de comparaison entre des approches supervisées et non supervisées, cette dernière s’est montrée plus approprié en raison du comportement indépendant par rapport à l’étape d’entraînement. Afin de confirmer la qualité de ces résultats quatre expériences ont été menées. Lors d’une première expérience deux algorithmes ont été évalués pour application de regroupements en utilisant l’approche FCC (False Colour Composite). Cet essai a permis d’observer une vitesse de convergence, jusqu’a vingt fois plus rapide, ainsi qu’une efficacité significativement accrue concernant l’identification en comparaison des résultats de la littérature. Cependant des essais effectués sur des données LWIR ont montré un manque de prédiction de la surface du grain lorsque les grains étaient irréguliers avec présence d’agrégats minéraux. La seconde expérience a consisté, en une analyse quantitaive comparative entre deux bases de données de Ground Truth (GT), nommée rigid-GT et observed-GT (rigide-GT: étiquet manuel de la région, observée-GT:étiquetage manuel les pixels). La précision des résultats était 1.5 fois meilleur lorsque l’on a utlisé la base de données observed-GT que rigid-GT. Pour les deux dernières epxérience, des données venant d’un MEB (Microscope Électronique à Balayage) ainsi que d’un microscopie à fluorescence (XRF) ont été ajoutées. Ces données ont permis d’introduire des informations relatives tant aux agrégats minéraux qu’à la surface des grains. Les résultats ont été comparés par des techniques d’identification automatique des minéraux, utilisant ArcGIS. Cette dernière a montré une performance prometteuse quand à l’identification automatique et à aussi été utilisée pour la GT de validation. Dans l’ensemble, les quatre méthodes de cette thèse représentent des méthodologies bénéfiques pour l’identification des minéraux. Ces méthodes présentent l’avantage d’être non-destructives, relativement précises et d’avoir un faible coût en temps calcul ce qui pourrait les qualifier pour être utilisée dans des conditions de laboratoire ou sur le terrain.The geological applications of hyperspectral infrared imagery mainly consist in mineral identification, mapping, airborne or portable instruments, and core logging. Finding the mineral indicators offer considerable benefits in terms of mineralogy and mineral exploration which usually involves application of portable instrument and core logging. Moreover, faster and more mechanized systems development increases the precision of identifying mineral indicators and avoid any possible mis-classification. Therefore, the objective of this thesis was to create a tool to using hyperspectral infrared imagery and process the data through image analysis and machine learning methods to identify small size mineral grains used as mineral indicators. This system would be applied for different circumstances to provide an assistant for geological analysis and mineralogy exploration. The experiments were conducted in laboratory conditions in the long-wave infrared (7.7μm to 11.8μm - LWIR), with a LWIR-macro lens (to improve spatial resolution), an Infragold plate, and a heating source. The process began with a method to calculate the continuum removal. The approach is the application of Non-negative Matrix Factorization (NMF) to extract Rank-1 NMF and estimate the down-welling radiance and then compare it with other conventional methods. The results indicate successful suppression of the continuum from the spectra and enable the spectra to be compared with spectral libraries. Afterwards, to have an automated system, supervised and unsupervised approaches have been tested for identification of pyrope, olivine and quartz grains. The results indicated that the unsupervised approach was more suitable due to independent behavior against training stage. Once these results obtained, two algorithms were tested to create False Color Composites (FCC) applying a clustering approach. The results of this comparison indicate significant computational efficiency (more than 20 times faster) and promising performance for mineral identification. Finally, the reliability of the automated LWIR hyperspectral infrared mineral identification has been tested and the difficulty for identification of the irregular grain’s surface along with the mineral aggregates has been verified. The results were compared to two different Ground Truth(GT) (i.e. rigid-GT and observed-GT) for quantitative calculation. Observed-GT increased the accuracy up to 1.5 times than rigid-GT. The samples were also examined by Micro X-ray Fluorescence (XRF) and Scanning Electron Microscope (SEM) in order to retrieve information for the mineral aggregates and the grain’s surface (biotite, epidote, goethite, diopside, smithsonite, tourmaline, kyanite, scheelite, pyrope, olivine, and quartz). The results of XRF imagery compared with automatic mineral identification techniques, using ArcGIS, and represented a promising performance for automatic identification and have been used for GT validation. In overall, the four methods (i.e. 1.Continuum removal methods; 2. Classification or clustering methods for mineral identification; 3. Two algorithms for clustering of mineral spectra; 4. Reliability verification) in this thesis represent beneficial methodologies to identify minerals. These methods have the advantages to be a non-destructive, relatively accurate and have low computational complexity that might be used to identify and assess mineral grains in the laboratory conditions or in the field

The contribution of multitemporal information from multispectral satellite images for automatic land cover classification at the national scale

Thesis submitted to the Instituto Superior de Estatística e Gestão de Informação da Universidade Nova de Lisboa in partial fulfillment of the requirements for the Degree of Doctor of Philosophy in Information Management – Geographic Information SystemsImaging and sensing technologies are constantly evolving so that, now, the latest generations of satellites commonly provide with Earth’s surface snapshots at very short sampling periods (i.e. daily images). It is unquestionable that this tendency towards continuous time observation will broaden up the scope of remotely sensed activities. Inevitable also, such increasing amount of information will prompt methodological approaches that combine digital image processing techniques with time series analysis for the characterization of land cover distribution and monitoring of its dynamics on a frequent basis. Nonetheless, quantitative analyses that convey the proficiency of three-dimensional satellite images data sets (i.e. spatial, spectral and temporal) for the automatic mapping of land cover and land cover time evolution have not been thoroughly explored. In this dissertation, we investigate the usefulness of multispectral time series sets of medium spatial resolution satellite images for the regular land cover characterization at the national scale. This study is carried out on the territory of Continental Portugal and exploits satellite images acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) and MEdium Resolution Imaging Spectrometer (MERIS). In detail, we first focus on the analysis of the contribution of multitemporal information from multispectral satellite images for the automatic land cover classes’ discrimination. The outcomes show that multispectral information contributes more significantly than multitemporal information for the automatic classification of land cover types. In the sequence, we review some of the most important steps that constitute a standard protocol for the automatic land cover mapping from satellite images. Moreover, we delineate a methodological approach for the production and assessment of land cover maps from multitemporal satellite images that guides us in the production of a land cover map with high thematic accuracy for the study area. Finally, we develop a nonlinear harmonic model for fitting multispectral reflectances and vegetation indices time series from satellite images for numerous land cover classes. The simplified multitemporal information retrieved with the model proves adequate to describe the main land cover classes’ characteristics and to predict the time evolution of land cover classes’individuals

Satellite image classification and spatial analysis of agricultural areas for land cover mapping of grizzly bear habitat

Habitat loss and human-caused mortality are the most serious threats facing grizzly bear (Ursus arctosi L.) populations in Alberta, with conflicts between people and bears in agricultural areas being especially important. For this reason, information is needed about grizzly bears in agricultural areas. The objectives of this research were to find the best possible classification approach for determining multiple classes of agricultural and herbaceous land cover for the purpose of grizzly bear habitat mapping, and to determine what, if any, spatial and compositional components of the landscape affected the bears in these agricultural areas. Spectral and environmental data for five different land-cover types of interest were acquired in late July, 2007, from Landsat Thematic Mapper satellite imagery and field data collection in two study areas in Alberta. Three different classification methods were analyzed, the best method being the Supervised Sequential Masking (SSM) technique, which gave an overall accuracy of 88% and a Kappa Index of Agreement (KIA) of 83%. The SSM classification was then expanded to cover 6 more Landsat scenes, and combined with bear GPS location data. Analysis of this data revealed that bears in agricultural areas were found in grasses / forage crops 77% of the time, with small grains and bare soil / fallow fields making up the rest of the visited land-cover. Locational data for 8 bears were examined in an area southwest of Calgary, Alberta. The 4494 km2 study area was divided into 107 sub-landscapes of 42 km2. Five-meter spatial resolution IRS panchromatic imagery was used to classify the area and derive compositional and configurational metrics for each sub-landscape. It was found that the amount of agricultural land did not explain grizzly bear use; however, secondary effects of agriculture on landscape configuration did. High patch density and variation in distances between neighboring similar patch types were seen as the most significant metrics in the abundance models; higher variation in patch shape, greater contiguity between patches, and lower average distances between neighboring similar patches were the most consistently significant predictors in the bear presence / absence models. Grizzly bears appeared to prefer areas that were structurally correlated to natural areas, and avoided areas that were structurally correlated to agricultural areas. Grizzly bear presence could be predicted in a particular sub-landscape with 87% accuracy using a logistic regression model. Between 30% and 35% of the grizzlies‟ landscape scale habitat selection was explained