90 research outputs found

    Cartografía de severidad de incendios forestales a partir de la combinación del modelo de mezclas espectrales y la clasificación basada en objetos

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    This study shows an accurate and fast methodology in order to evaluate fire severity classes of large forest fires. A single Landsat Enhanced Thematic Mapper multispectral image was utilized in this study with the aim of mapping fire severity classes (high, moderate and low) using a combined-approach based in an spectral mixing model and object-based image analysis. A large wildfire in the Northwest of Spain is used to test the model. Fraction images obtained by Landsat unmixing were used as input data in the object-based image analysis. A multilevel segmentation and a classification were carried out by using membership functions. This method was compared with other simplest ones in order to evaluate the suitability to distinguish between the three fire severity classes above mentioned. McNemar’s test was used to evaluate the statistical significance of the difference between approaches tested in this study. The combined approach achieved the highest accuracy reaching 97.32% and kappa index of agreement of 95.96% and improving accuracy of individual classes.Este estudio presenta una metodología rápida y precisa para la evaluación de los niveles de severidad que afectan a grandes incendios forestales. El trabajo combina un modelo de mezclas espectrales y un análisis de imágenes basado en objetos con el objetivo de cartografiar distintos niveles de severidad (alto, moderado y bajo) empleando una imagen multiespectral Landsat Enhanced Thematic Mapper. Este modelo es testado en un gran incendio forestal ocurrido en el noroeste de España. Las imágenes fracción obtenidas tras aplicar el modelo de mezclas a la imagen Landsat fueron utilizadas como datos de entrada en el análisis basado en objetos. En este se llevó a cabo una segmentación multinivel y una posterior clasificación usando funciones de pertenencia. Esta metodología fue comparada con otras más simples con el fin de evaluar su conveniencia a al hora de distinguir entre los tres niveles de severidad anteriormente mencionados. El test de McNemar fue empleado para evaluar la significancia estadística de la diferencia entre los métodos testados en el estudio. El método combinado alcanzó la más alta precisión con un 97,32% y un índice Kappa del 95,96%, además de mejorar la precisión de los niveles individualmente

    MIDAS prototype Multispectral Interactive Digital Analysis System for large area earth resources surveys. Volume 2: Charge coupled device investigation

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    MIDAS is a third-generation, fast, low cost, multispectral recognition system able to keep pace with the large quantity and high rates of data acquisition from large regions with present and projected sensors. MIDAS, for example, can process a complete ERTS frame in forty seconds and provide a color map of sixteen constituent categories in a few minutes. A principal objective of the MIDAS Program is to provide a system well interfaced with the human operator and thus to obtain large overall reductions in turn-around time and significant gains in throughput. The need for advanced onboard spacecraft processing of remotely sensed data is stated and approaches to this problem are described which are feasible through the use of charge coupled devices. Tentative mechanizations for the required processing operations are given in large block form. These initial designs can serve as a guide to circuit/system designers

    Self Designing Pattern Recognition System Employing Multistage Classification

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    Recently, pattern recognition/classification has received a considerable attention in diverse engineering fields such as biomedical imaging, speaker identification, fingerprint recognition, etc. In most of these applications, it is desirable to maintain the classification accuracy in the presence of corrupted and/or incomplete data. The quality of a given classification technique is measured by the computational complexity, execution time of algorithms, and the number of patterns that can be classified correctly despite any distortion. Some classification techniques that are introduced in the literature are described in Chapter one. In this dissertation, a pattern recognition approach that can be designed to have evolutionary learning by developing the features and selecting the criteria that are best suited for the recognition problem under consideration is proposed. Chapter two presents some of the features used in developing the set of criteria employed by the system to recognize different types of signals. It also presents some of the preprocessing techniques used by the system. The system operates in two modes, namely, the learning (training) mode, and the running mode. In the learning mode, the original and preprocessed signals are projected into different transform domains. The technique automatically tests many criteria over the range of parameters for each criterion. A large number of criteria are developed from the features extracted from these domains. The optimum set of criteria, satisfying specific conditions, is selected. This set of criteria is employed by the system to recognize the original or noisy signals in the running mode. The modes of operation and the classification structures employed by the system are described in details in Chapter three. The proposed pattern recognition system is capable of recognizing an enormously large number of patterns by virtue of the fact that it analyzes the signal in different domains and explores the distinguishing characteristics in each of these domains. In other words, this approach uses available information and extracts more characteristics from the signals, for classification purposes, by projecting the signal in different domains. Some experimental results are given in Chapter four showing the effect of using mathematical transforms in conjunction with preprocessing techniques on the classification accuracy. A comparison between some of the classification approaches, in terms of classification rate in case of distortion, is also given. A sample of experimental implementations is presented in chapter 5 and chapter 6 to illustrate the performance of the proposed pattern recognition system. Preliminary results given confirm the superior performance of the proposed technique relative to the single transform neural network and multi-input neural network approaches for image classification in the presence of additive noise

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

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    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

    Multi-image classification and compression using vector quantization

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    Vector Quantization (VQ) is an image processing technique based on statistical clustering, and designed originally for image compression. In this dissertation, several methods for multi-image classification and compression based on a VQ design are presented. It is demonstrated that VQ can perform joint multi-image classification and compression by associating a class identifier with each multi-spectral signature codevector. We extend the Weighted Bayes Risk VQ (WBRVQ) method, previously used for single-component images, that explicitly incorporates a Bayes risk component into the distortion measure used in the VQ quantizer design and thereby permits a flexible trade-off between classification and compression priorities. In the specific case of multi-spectral images, we investigate the application of the Multi-scale Retinex algorithm as a preprocessing stage, before classification and compression, that performs dynamic range compression, reduces the dependence on lighting conditions, and generally enhances apparent spatial resolution. The goals of this research are four-fold: (1) to study the interrelationship between statistical clustering, classification and compression in a multi-image VQ context; (2) to study mixed-pixel classification and combined classification and compression for simulated and actual, multispectral and hyperspectral multi-images; (3) to study the effects of multi-image enhancement on class spectral signatures; and (4) to study the preservation of scientific data integrity as a function of compression. In this research, a key issue is not just the subjective quality of the resulting images after classification and compression but also the effect of multi-image dimensionality on the complexity of the optimal coder design

    Mediterranean Forest Species Mapping Using Hyperspectral Imagery

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    2011/2012Advances in hyperspectral technology provides scientists the opportunity to investigate problems that were difficult if not impossible to approach using multispectral data; among those, species composition which is a very important and dynamic forest parameter, linked with many environmental qualities that we want to map and monitor. This study addresses the problem of Mediterranean forest species mapping using satellite EO-1 Hyperion imagery (30m, 196 bands). Two pixel based techniques were evaluated, namely Spectral Angle Mapper (SAM) and Support Vector Machines (SVM), as well as an object oriented approach (GEOBIA). These techniques were applied in two study areas with different species composition and pattern complexity, namely Thasos and Taksiarchis. Extensive field work provided reference data for the accuracy assessment of the produced maps. Image preprocessing included several steps of data corrections and the Minimum Noise Fraction transformation, as means for data dimensionality reduction. In the case of Thasos, where two conifer species are present, SAM technique resulted in an overall accuracy (OA) of 3.9%, SVM technique yielded OA of 89.0% and GEOBIA achieved an OA of 85.3%. In the case of Taksiarchis, where more species are present – both conifers and broadleaved- the respective OA was 80.0%, 82.6% and 74.1%. All three methodologies implemented to investigate the value of hyperspectral imagery in Mediterranean forest species mapping, achieved very accurate results; in some cases equivalent to forest inventory maps. SAM was the straightest forward to implement, only depending on the training samples. Implementation SVM involved the specification of several parameters as well as the use of custom software and was more successful in the challenging landscape of Taksiarchis. GEOBIA adapted to scale through segmentation and extended the exercise of classification, allowing for knowledge based refinement. Lower accuracies could be attributed to the assessment method, as research on alternative assessment methods better adapted to the nature of object space is ongoing. Two typical Mediterranean forests were studied. In Thasos, two conifer species of the same genus, namely Pinus brutia and Pinus nigra, dominate a big part of the island. Both of them were accurately mapped by all methodologies. In Taksiarchis primarily stands of Quercus frainetto mix with stands of Fagus sylvatica and the aforementioned pines. The two pines were again mapped with high accuracy. However, there was a notable confusion between the two broadleaved species, indicating the need for further research, possibly taking advantage of species phenology. The outcome of the proposed methodologies could confidently meet the current needs for vegetation geographical data in regional to national scale, and demonstrate the value of hyperspectral imagery in Mediterranean forest species mapping.XXIII Ciclo198

    On the Use of Imaging Spectroscopy from Unmanned Aerial Systems (UAS) to Model Yield and Assess Growth Stages of a Broadacre Crop

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    Snap bean production was valued at $363 million in 2018. Moreover, the increasing need in food production, caused by the exponential increase in population, makes this crop vitally important to study. Traditionally, harvest time determination and yield prediction are performed by collecting limited number of samples. While this approach could work, it is inaccurate, labor-intensive, and based on a small sample size. The ambiguous nature of this approach furthermore leaves the grower with under-ripe and over-mature plants, decreasing the final net profit and the overall quality of the product. A more cost-effective method would be a site-specific approach that would save time and labor for farmers and growers, while providing them with exact detail to when and where to harvest and how much is to be harvested (while forecasting yield). In this study we used hyperspectral (i.e., point-based and image-based), as well as biophysical data, to identify spectral signatures and biophysical attributes that could schedule harvest and forecast yield prior to harvest. Over the past two decades, there have been immense advances in the field of yield and harvest modeling using remote sensing data. Nevertheless, there still exists a wide gap in the literature covering yield and harvest assessment as a function of time using both ground-based and unmanned aerial systems. There is a need for a study focusing on crop-specific yield and harvest assessment using a rapid, affordable system. We hypothesize that a down-sampled multispectral system, tuned with spectral features identified from hyperspectral data, could address the mentioned gaps. Moreover, we hypothesize that the airborne data will contain noise that could negatively impact the performance and the reliability of the utilized models. Thus, We address these knowledge gaps with three objectives as below: 1. Assess yield prediction of snap bean crop using spectral and biophysical data and identify discriminating spectral features via statistical and machine learning approaches. 2. Evaluate snap bean harvest maturity at both the plant growth stage and pod maturity level, by means of spectral and biophysical indicators, and identify the corresponding discriminating spectral features. 3. Assess the feasibility of using a deep learning architecture for reducing noise in the hyperspectral data. In the light of the mentioned objectives, we carried out a greenhouse study in the winter and spring of 2019, where we studied temporal change in spectra and physical attributes of snap-bean crop, from Huntington cultivar, using a handheld spectrometer in the visible- to shortwave-infrared domain (400-2500 nm). Chapter 3 of this dissertation focuses on yield assessment of the greenhouse study. Findings from this best-case scenario yield study showed that the best time to study yield is approximately 20-25 days prior to harvest that would give out the most accurate yield predictions. The proposed approach was able to explain variability as high as R2 = 0.72, with spectral features residing in absorption regions for chlorophyll, protein, lignin, and nitrogen, among others. The captured data from this study contained minimal noise, even in the detector fall-off regions. Moving the focus to harvest maturity assessment, Chapter 4 presents findings from this objective in the greenhouse environment. Our findings showed that four stages of maturity, namely vegetative growth, budding, flowering, and pod formation, are distinguishable with 79% and 78% accuracy, respectively, via the two introduced vegetation indices, as snap-bean growth index (SGI) and normalized difference snap-bean growth index (NDSI), respectively. Moreover, pod-level maturity classification showed that ready-to-harvest and not-ready-to-harvest pods can be separated with 78% accuracy with identified wavelengths residing in green, red edge, and shortwave-infrared regions. Moreover, Chapters 5 and 6 focus on transitioning the learned concepts from the mentioned greenhouse scenario to UAS domain. We transitioned from a handheld spectrometer in the visible to short-wave infrared domain (400-2500 nm) to a UAS-mounted hyperspectral imager in the visible-to-near-infrared region (400-1000 nm). Two years worth of data, at two different geographical locations, were collected in upstate New York and examined for yield modeling and harvest scheduling objectives. For analysis of the collected data, we introduced a feature selection library in Python, named “Jostar”, to identify the most discriminating wavelengths. The findings from the yield modeling UAS study show that pod weight and seed length, as two different yield indicators, can be explained with R2 as high as 0.93 and 0.98, respectively. Identified wavelengths resided in blue, green, red, and red edge regions, and 44-55 days after planting (DAP) showed to be the optimal time for yield assessment. Chapter 6, on the other hand, evaluates maturity assessment, in terms of pod classification, from the UAS perspective. Results from this study showed that the identified features resided in blue, green, red, and red-edge regions, contributing to F1 score as high as 0.91 for differentiating between ready-to-harvest vs. not ready-to-harvest. The identified features from this study is in line with those detected from the UAS yield assessment study. In order to have a parallel comparison of the greenhouse study against the UAS study, we adopted the methodology employed for UAS studies and applied it to the greenhouse studies, in Chapter 7. Since the greenhouse data were captured in the visible-to-shortwave-infrared (400-2500 nm) domain, and the UAS study data were captured in the VNIR (400-1000 nm) domain, we truncated the spectral range of the collected data from the greenhouse study to the VNIR domain. The comparison experiment between the greenhouse study and the UAS studies for yield assessment, at two harvest stages early and late, showed that spectral features in 450-470, 500-520, 650, 700-730 nm regions were repeated on days with highest coefficient of determination. Moreover, 46-48 DAP with high coefficient of determination for yield prediction were repeated in five out of six data sets (two early stages, each three data sets). On the other hand, the harvest maturity comparison between the greenhouse study and the UAS data sets showed that similar identified wavelengths reside in ∼450, ∼530, ∼715, and ∼760 nm regions, with performance metric (F1 score) of 0.78, 0.84, and 0.9 for greenhouse, 2019 UAS, and 2020 UAS data, respectively. However, the incorporated noise in the captured data from the UAS study, along with the high computational cost of the classical mathematical approach employed for denoising hyperspectral data, have inspired us to leverage the computational performance of hyperspectral denoising by assessing the feasibility of transferring the learned concepts to deep learning models. In Chapter 8, we approached hyperspectral denoising in spectral domain (1D fashion) for two types of noise, integrated noise and non-independent and non-identically distributed (non-i.i.d.) noise. We utilized Memory Networks due to their power in image denoising for hyperspectral denoising, introduced a new loss and benchmarked it against several data sets and models. The proposed model, HypeMemNet, ranked first - up to 40% in terms of signal-to-noise ratio (SNR) for resolving integrated noise, and first or second, by a small margin for resolving non-i.i.d. noise. Our findings showed that a proper receptive field and a suitable number of filters are crucial for denoising integrated noise, while parameter size was shown to be of the highest importance for non-i.i.d. noise. Results from the conducted studies provide a comprehensive understanding encompassing yield modeling, harvest scheduling, and hyperspectral denoising. Our findings bode well for transitioning from an expensive hyperspectral imager to a multispectral imager, tuned with the identified bands, as well as employing a rapid deep learning model for hyperspectral denoising

    Airborne remote sensing of estuarine intertidal radionuclide concentrations

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    The ability to map industrial discharges through remote sensing provides a powerful tool in environmental monitoring. Radionuclide effluents have been discharged, under authorization, into the Irish Sea from BNFL (British Nuclear Fuels Plc.) sites at Sellafield and Springfields since 1952. The quantitative mapping of this anthropogenic radioactivity in estuarine intertidal zones is crucial for absolute interpretations of radionuclide transport. The spatial resolutions of traditional approaches e.g. point sampling and airborne gamma surveys are insufficient to support geomorphic interpretations of the fate of radionuclides in estuaries. The research presented in this thesis develops the use of airborne remote sensing to derive high-resolution synoptic data on the distribution of anthropogenic radionuclides in the intertidal areas of the Ribble Estuary, Lancashire, UK. From multidate surface sediment samples a significant relationship was identified between the Sellafieldderived 137Cs & 241Am and clay content (r2=0.93 & 0.84 respectively). Detailed in situ, and laboratory, reflectance (0.4-2.5mn) experiments demonstrated that significant relationships exist between Airborne Thematic Mapper (ATM) simulated reflectance and intertidal sediment grain-size. The spectral influence of moisture on the reflectance characteristics of the intertidal area is also evident. This had substantial implications for the timing of airborne image acquisition. Low-tide Daedalus ATM imagery (Natural Environmental Research Council) was collected of the Ribble Estuary on May 30th 1997. Preprocessing and linear unmixing of the imagery allowed accurate sub-pixel determinations of sediment clay content distributions (r2=0.8 1). Subsequently, the established relationships between 137Cs & 241Am and sediment grain-size enabled the radionuclide activity distributions across the entire intertidal area (92km2) to be mapped at a geomorphic scale (1.75m). The accuracy of these maps was assessed by comparison with in situ samples and the results of previous radiological studies within the estuary. Finally, detailed conclusions are made regarding radionuclide sinks and sources, and surface activity redistribution within the Ribble Estuary environment

    An investigation in the use of advanced remote sensing and geographic information system techniques for post-fire impact assessment on vegetation.

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    2006/2007Gli incendi boschivi rappresentano uno dei maggiori problemi ambientali nella regione Mediterranea con vaste superfici colpite ogni estate. Una stima dell’impatto ambientale degli incendi (a breve e a lungo termine) richiede la raccolta di informazioni accurate post-incendio relative al tipo di incendio, all’intensità, alla rigenerazione forestale ed al ripristino della vegetazione. L’utilizzo di tecniche avanzate di telerilevamento può fornire un valido strumento per lo studio di questi fenomeni. L’importanza di queste ricerche è stata più volte sottolineata dalla Commissione Europea che si è concentrata sullo studio degli incendi boschivi ed il loro effetto sulla vegetazione attraverso lo sviluppo di adeguati metodi di stima dell’impatto e di mitigazione. Scopo di questo lavoro è la stima dell’impatto post-incendio sulla vegetazione in ambiente Mediterraneo per mezzo di immagini satellitari ad alta risoluzione, di rilievi a terra e mediante tecniche avanzate di analisi dei dati. Il lavoro ha riguardato lo sviluppo di un sistema per l’integrazione di dati telerilevati ad altissima risoluzione spaziale e spettrale. Per la stima dell’impatto a breve termine, un modello di classificazione ad oggetti è stato sviluppato utilizzando immagini Ikonos ad altissima risoluzione spaziale per cartografare il tipo di incendio, differenziando l’incendio radente dall’incendio di chioma. I risultati mostrano che la classificazione ad oggetti potrebbe essere utilizzata per distinguere con elevata accuratezza (87% di accuratezza complessiva) le due tipologie di incendio, in particolare nei boschi Mediterranei aperti. È stata inoltre valutata la capacità della classificazione ad oggetti di distinguere e cartografare tre livelli di intensità del fuoco utilizzando le immagini Ikonos e l’accuratezza del risultato è stimata all’ 83%. Per la stima dell’impatto a lungo termine, la mappatura della rigenerazione post-incendio (pino) e la ripresa della vegetazione arbustiva sono state valutate mediante tre approcci: 1) la classificazione ad oggetti di immagini ad altissima risoluzione QuickBird che ha permesso di mappare la ripresa della vegetazione e l’impatto sulla copertura a seguito dell’incendio distinguendo due livelli di intensità dell’incendio (accuratezza della classificazione 86%). 2) l’analisi statistica di dati iperspettrali rilevati in campo che ha permesso una riduzione del 97% del volume di dati e la selezione delle migliori 14 bande per discriminare l’età e le specie di pino e le 18 migliori bande per la caratterizzazione delle specie arbustive. Successivamente, i dati iperspettrali Hyperion sono stati utlizzati per mappare la rigenerazione forestale e la ripresa della vegetazione. L’accuratezza complessiva della classificazione è stata del 75.1% considerando due diverse specie di pino ed altre specie vegetali. 3) una classificazione ad oggetti che ha combinato l’analisi dei dati QuickBird ed Hyperion. Si è registrato un aumento dell’accuratezza della classificazione pari all’8.06% rispetto all’utilizzo dei soli dati Hyperion. Complessivamente, si osserva che strumenti avanzati di telerilevamento consentono di raccogliere le informazioni relative alle aree incendiate, la rigenerazione forestale e la ripresa della vegetazione in modo accurato e vantaggioso in termini di costi e tempi.Forest fires are a major environmental problem in the Mediterranean region, where large areas are affected each summer. An assessment of the environmental impact of forest fires (in the short-term and in the long-term) requires the collection of accurate and detailed post-fire information related to fire type, fire severity, forest regeneration and vegetation recovery. Advanced tools in remote sensing provide a powerful tool for the study of this phenomenon. The importance of this work was often emphasized by the European Commission, which focused on the studying of forest fires and their effect on vegetation through the development of appropriate impact assessment and mitigation methods. The aim of this study was to assess the post-fire impact on vegetation in a Mediterranean environment by employing high quality satellite and field data and by using advanced data processing techniques. The work entailed the development of a whole system integrating very high spatial and spectral resolution remotely sensed data. For short-term impact assessment, an object-oriented model was developed using very high spatial resolution Ikonos imagery to map the type of fire, namely, canopy fire and surface fire. The results showed that object-oriented classification could be used to accurately distinguish and map areas of surface and crown fire spread (overall accuracy of 87%), especially that occurring in open Mediterranean forests. Also, the performance of object-based classification in mapping three levels of fire severity by employing high spatial resolution Ikonos imagery was evaluated, and accuracy of the obtained results was estimated to be 83%. As for long-term impact assessment, the mapping of post-fire forest regeneration (pine) and vegetation recovery (shrub) was performed by following three different approaches. First, the developed object-based classification of QuickBird (very high spatial resolution) allowed post-fire vegetation recovery and survival mapping of canopy within two different fire severity levels (86% of classification accuracy). The main effect of fire has been to create a more homogeneous landscape. Second, statistical analysis of field hyperspectral data allowed a 97% reduction in data volume and recommended 14 best narrowbands to discriminate among pine trees (age and species) and 18 bands that best characterize the different shrub species. Then, hyperspectral Hyperion was employed for mapping post-fire forest regeneration and vegetation recovery. The overall classification accuracy was found to be 75.81% when mapping two different regenerated pine species and other species of vegetation recovery. Third, an object-oriented combined analysis of QuickBird and Hyperion was investigated for the same objective. An improvement in classification accuracy of 8.06% was recorded when combining both Hyperion and QuickBird imageries than by using only the Hyperion image. Overall, it was observed that advanced tools in remote sensing provided the necessary means for gathering information about the burned areas, the regenerated forests and the recovered vegetations in a successful and a timely/cost effective manner.XX Ciclo197
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