Pre-processing, classification and semantic querying of large-scale Earth observation spaceborne/airborne/terrestrial image databases: Process and product innovations.

By definition of Wikipedia, “big data is the term adopted for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The big data challenges typically include capture, curation, storage, search, sharing, transfer, analysis and visualization”. Proposed by the intergovernmental Group on Earth Observations (GEO), the visionary goal of the Global Earth Observation System of Systems (GEOSS) implementation plan for years 2005-2015 is systematic transformation of multisource Earth Observation (EO) “big data” into timely, comprehensive and operational EO value-adding products and services, submitted to the GEO Quality Assurance Framework for Earth Observation (QA4EO) calibration/validation (Cal/Val) requirements. To date the GEOSS mission cannot be considered fulfilled by the remote sensing (RS) community. This is tantamount to saying that past and existing EO image understanding systems (EO-IUSs) have been outpaced by the rate of collection of EO sensory big data, whose quality and quantity are ever-increasing. This true-fact is supported by several observations. For example, no European Space Agency (ESA) EO Level 2 product has ever been systematically generated at the ground segment. By definition, an ESA EO Level 2 product comprises a single-date multi-spectral (MS) image radiometrically calibrated into surface reflectance (SURF) values corrected for geometric, atmospheric, adjacency and topographic effects, stacked with its data-derived scene classification map (SCM), whose thematic legend is general-purpose, user- and application-independent and includes quality layers, such as cloud and cloud-shadow. Since no GEOSS exists to date, present EO content-based image retrieval (CBIR) systems lack EO image understanding capabilities. Hence, no semantic CBIR (SCBIR) system exists to date either, where semantic querying is synonym of semantics-enabled knowledge/information discovery in multi-source big image databases. In set theory, if set A is a strict superset of (or strictly includes) set B, then A B. This doctoral project moved from the working hypothesis that SCBIR computer vision (CV), where vision is synonym of scene-from-image reconstruction and understanding EO image understanding (EO-IU) in operating mode, synonym of GEOSS ESA EO Level 2 product human vision. Meaning that necessary not sufficient pre-condition for SCBIR is CV in operating mode, this working hypothesis has two corollaries. First, human visual perception, encompassing well-known visual illusions such as Mach bands illusion, acts as lower bound of CV within the multi-disciplinary domain of cognitive science, i.e., CV is conditioned to include a computational model of human vision. Second, a necessary not sufficient pre-condition for a yet-unfulfilled GEOSS development is systematic generation at the ground segment of ESA EO Level 2 product. Starting from this working hypothesis the overarching goal of this doctoral project was to contribute in research and technical development (R&D) toward filling an analytic and pragmatic information gap from EO big sensory data to EO value-adding information products and services. This R&D objective was conceived to be twofold. First, to develop an original EO-IUS in operating mode, synonym of GEOSS, capable of systematic ESA EO Level 2 product generation from multi-source EO imagery. EO imaging sources vary in terms of: (i) platform, either spaceborne, airborne or terrestrial, (ii) imaging sensor, either: (a) optical, encompassing radiometrically calibrated or uncalibrated images, panchromatic or color images, either true- or false color red-green-blue (RGB), multi-spectral (MS), super-spectral (SS) or hyper-spectral (HS) images, featuring spatial resolution from low (> 1km) to very high (< 1m), or (b) synthetic aperture radar (SAR), specifically, bi-temporal RGB SAR imagery. The second R&D objective was to design and develop a prototypical implementation of an integrated closed-loop EO-IU for semantic querying (EO-IU4SQ) system as a GEOSS proof-of-concept in support of SCBIR. The proposed closed-loop EO-IU4SQ system prototype consists of two subsystems for incremental learning. A primary (dominant, necessary not sufficient) hybrid (combined deductive/top-down/physical model-based and inductive/bottom-up/statistical model-based) feedback EO-IU subsystem in operating mode requires no human-machine interaction to automatically transform in linear time a single-date MS image into an ESA EO Level 2 product as initial condition. A secondary (dependent) hybrid feedback EO Semantic Querying (EO-SQ) subsystem is provided with a graphic user interface (GUI) to streamline human-machine interaction in support of spatiotemporal EO big data analytics and SCBIR operations. EO information products generated as output by the closed-loop EO-IU4SQ system monotonically increase their value-added with closed-loop iterations

GeoAI-enhanced Techniques to Support Geographical Knowledge Discovery from Big Geospatial Data

abstract: Big data that contain geo-referenced attributes have significantly reformed the way that I process and analyze geospatial data. Compared with the expected benefits received in the data-rich environment, more data have not always contributed to more accurate analysis. “Big but valueless” has becoming a critical concern to the community of GIScience and data-driven geography. As a highly-utilized function of GeoAI technique, deep learning models designed for processing geospatial data integrate powerful computing hardware and deep neural networks into various dimensions of geography to effectively discover the representation of data. However, limitations of these deep learning models have also been reported when People may have to spend much time on preparing training data for implementing a deep learning model. The objective of this dissertation research is to promote state-of-the-art deep learning models in discovering the representation, value and hidden knowledge of GIS and remote sensing data, through three research approaches. The first methodological framework aims to unify varied shadow into limited number of patterns, with the convolutional neural network (CNNs)-powered shape classification, multifarious shadow shapes with a limited number of representative shadow patterns for efficient shadow-based building height estimation. The second research focus integrates semantic analysis into a framework of various state-of-the-art CNNs to support human-level understanding of map content. The final research approach of this dissertation focuses on normalizing geospatial domain knowledge to promote the transferability of a CNN’s model to land-use/land-cover classification. This research reports a method designed to discover detailed land-use/land-cover types that might be challenging for a state-of-the-art CNN’s model that previously performed well on land-cover classification only.Dissertation/ThesisDoctoral Dissertation Geography 201

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

Single image super resolution for spatial enhancement of hyperspectral remote sensing imagery

Hyperspectral Imaging (HSI) has emerged as a powerful tool for capturing detailed spectral information across various applications, such as remote sensing, medical imaging, and material identification. However, the limited spatial resolution of acquired HSI data poses a challenge due to hardware and acquisition constraints. Enhancing the spatial resolution of HSI is crucial for improving image processing tasks, such as object detection and classification. This research focuses on utilizing Single Image Super Resolution (SISR) techniques to enhance HSI, addressing four key challenges: the efficiency of 3D Deep Convolutional Neural Networks (3D-DCNNs) in HSI enhancement, minimizing spectral distortions, tackling data scarcity, and improving state-of-the-art performance. The thesis establishes a solid theoretical foundation and conducts an in-depth literature review to identify trends, gaps, and future directions in the field of HSI enhancement. Four chapters present novel research targeting each of the aforementioned challenges. All experiments are performed using publicly available datasets, and the results are evaluated both qualitatively and quantitatively using various commonly used metrics. The findings of this research contribute to the development of a novel 3D-CNN architecture known as 3D Super Resolution CNN 333 (3D-SRCNN333). This architecture demonstrates the capability to enhance HSI with minimal spectral distortions while maintaining acceptable computational cost and training time. Furthermore, a Bayesian-optimized hybrid spectral spatial loss function is devised to improve the spatial quality and minimize spectral distortions, combining the best characteristics of both domains. Addressing the challenge of data scarcity, this thesis conducts a thorough study on Data Augmentation techniques and their impact on the spectral signature of HSI. A new Data Augmentation technique called CutMixBlur is proposed, and various combinations of Data Augmentation techniques are evaluated to address the data scarcity challenge, leading to notable enhancements in performance. Lastly, the 3D-SRCNN333 architecture is extended to the frequency domain and wavelet domain to explore their advantages over the spatial domain. The experiments reveal promising results with the 3D Complex Residual SRCNN (3D-CRSRCNN), surpassing the performance of 3D-SRCNN333. The findings presented in this thesis have been published in reputable conferences and journals, indicating their contribution to the field of HSI enhancement. Overall, this thesis provides valuable insights into the field of HSI-SISR, offering a thorough understanding of the advancements, challenges, and potential applications. The developed algorithms and methodologies contribute to the broader goal of improving the spatial resolution and spectral fidelity of HSI, paving the way for further advancements in scientific research and practical implementations.Hyperspectral Imaging (HSI) has emerged as a powerful tool for capturing detailed spectral information across various applications, such as remote sensing, medical imaging, and material identification. However, the limited spatial resolution of acquired HSI data poses a challenge due to hardware and acquisition constraints. Enhancing the spatial resolution of HSI is crucial for improving image processing tasks, such as object detection and classification. This research focuses on utilizing Single Image Super Resolution (SISR) techniques to enhance HSI, addressing four key challenges: the efficiency of 3D Deep Convolutional Neural Networks (3D-DCNNs) in HSI enhancement, minimizing spectral distortions, tackling data scarcity, and improving state-of-the-art performance. The thesis establishes a solid theoretical foundation and conducts an in-depth literature review to identify trends, gaps, and future directions in the field of HSI enhancement. Four chapters present novel research targeting each of the aforementioned challenges. All experiments are performed using publicly available datasets, and the results are evaluated both qualitatively and quantitatively using various commonly used metrics. The findings of this research contribute to the development of a novel 3D-CNN architecture known as 3D Super Resolution CNN 333 (3D-SRCNN333). This architecture demonstrates the capability to enhance HSI with minimal spectral distortions while maintaining acceptable computational cost and training time. Furthermore, a Bayesian-optimized hybrid spectral spatial loss function is devised to improve the spatial quality and minimize spectral distortions, combining the best characteristics of both domains. Addressing the challenge of data scarcity, this thesis conducts a thorough study on Data Augmentation techniques and their impact on the spectral signature of HSI. A new Data Augmentation technique called CutMixBlur is proposed, and various combinations of Data Augmentation techniques are evaluated to address the data scarcity challenge, leading to notable enhancements in performance. Lastly, the 3D-SRCNN333 architecture is extended to the frequency domain and wavelet domain to explore their advantages over the spatial domain. The experiments reveal promising results with the 3D Complex Residual SRCNN (3D-CRSRCNN), surpassing the performance of 3D-SRCNN333. The findings presented in this thesis have been published in reputable conferences and journals, indicating their contribution to the field of HSI enhancement. Overall, this thesis provides valuable insights into the field of HSI-SISR, offering a thorough understanding of the advancements, challenges, and potential applications. The developed algorithms and methodologies contribute to the broader goal of improving the spatial resolution and spectral fidelity of HSI, paving the way for further advancements in scientific research and practical implementations

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Determining estuarine seagrass density measures from low altitude multispectral imagery flown by remotely piloted aircraft

Seagrass is the subject of significant conservation research. Seagrass is ecologically important and of significant value to human interests. Many seagrass species are thought to be in decline. Degradation of seagrass populations are linked to anthropogenic environmental issues. Effective management requires robust monitoring that is affordable at large scale. Remote sensing methods using satellite and aircraft imagery enable mapping of seagrass populations at landscape scale. Aerial monitoring of a seagrass population can require imagery of high spatial and/or spectral resolution for successful feature extraction across all levels of seagrass density. Remotely piloted aircraft (RPA) can operate close to the ground under precise flight control enabling repeated surveys in high detail with accurate revisit-positioning. This study evaluates a method for assessing intertidal estuarine seagrass (Zostera muelleri) presence/absence and coverage density using multispectral imagery collected by a remotely piloted aircraft (RPA) flying at 30 m above the estuary surface (2.7 cm ground sampling distance). The research was conducted at Wharekawa Harbour on the eastern coast of the Coromandel Peninsula, North Island, New Zealand. Differential drainage of residual ebb waters from the surface of an estuary at low tide creates a mosaic of drying sediment, draining surface and static shallow pooling that has potential to interfere with spectral observations. The field surveys demonstrated that despite minor shifts in the spectral coordinates of seagrass and other surface material, there was no apparent difference in image classification outcome from the time of bulk tidal water clearance to the time of returning tidal flood. For the survey specification tested, classification accuracy increased with decreasing segmentation scale. Pixel-based image analysis (PBIA) achieved higher classification accuracy than object-based image analysis (OBIA) assessed at a range of segmentation scales. Contaminating objects such as shells and detritus can become aggregated within polygon objects when OBIA is applied but remain as isolated objects under PBIA at this image resolution. There was clear separability of spectra for seagrass and sediment, but shell and detritus confounded the classification of seagrass density in some situations. High density seagrass was distinct from sediment, but classification error arose for sparse seagrass. Three classifiers (linear discriminant analysis, support vector machine and random forest) and three feature selection options (no selection, collinearity reduction and recursive feature elimination) were assessed for effect on classification performance. The random forest classifier yielded the highest classification accuracy, with no accuracy benefit gained from collinearity reduction or recursive feature elimination. Spectral vegetation indices and texture layers substantially improved classification accuracy. Object geometry made a negligible contribution to classification accuracy using mean-shift segmentation at this image-scale. The method achieved classification of seagrass density with up to 84% accuracy on a three-tier end-member class scale (low, medium, and high density) when using training data formed using visual interpretation of ground reference photography, and up to 93% accuracy using precisely measured seagrass leaf-area. Visual interpretation agreed with precisely measured seagrass leaf area 88% of the time with some misattribution at mid-density. Visual interpretation was substantially faster to apply than measuring the leaf area. A decile class scale for seagrass density correlated with actual leaf area measures more than the three-tier scale, however, was less accurate for absolute class attribution. The research demonstrates that seagrass feature extraction from RPA-flown imagery is a feasible and repeatable option for seagrass population monitoring and environmental reporting. Further calibration is required for whole- and multi-estuary application

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

COBE's search for structure in the Big Bang

The launch of Cosmic Background Explorer (COBE) and the definition of Earth Observing System (EOS) are two of the major events at NASA-Goddard. The three experiments contained in COBE (Differential Microwave Radiometer (DMR), Far Infrared Absolute Spectrophotometer (FIRAS), and Diffuse Infrared Background Experiment (DIRBE)) are very important in measuring the big bang. DMR measures the isotropy of the cosmic background (direction of the radiation). FIRAS looks at the spectrum over the whole sky, searching for deviations, and DIRBE operates in the infrared part of the spectrum gathering evidence of the earliest galaxy formation. By special techniques, the radiation coming from the solar system will be distinguished from that of extragalactic origin. Unique graphics will be used to represent the temperature of the emitting material. A cosmic event will be modeled of such importance that it will affect cosmological theory for generations to come. EOS will monitor changes in the Earth's geophysics during a whole solar color cycle

Semantic pre-classification of vegetation gradient based on linearly unmixed Landsat time series

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