Spectral Optimization of Airborne Multispectral Camera for Land Cover Classification: Automatic Feature Selection and Spectral Band Clustering

Hyperspectral imagery consists of hundreds of contiguous spectral bands. However, most of them are redundant. Thus a subset of well-chosen bands is generally sufficient for a specific problem, enabling to design adapted superspectral sensors dedicated to specific land cover classification. Related both to feature selection and extraction, spectral optimization identifies the most relevant band subset for specific applications, involving a band subset relevance score as well as a method to optimize it. This study first focuses on the choice of such relevance score. Several criteria are compared through both quantitative and qualitative analyses. To have a fair comparison, all tested criteria are compared to classic hyperspectral data sets using the same optimization heuristics: an incremental one to assess the impact of the number of selected bands and a stochastic one to obtain several possible good band subsets and to derive band importance measures out of intermediate good band subsets. Last, a specific approach is proposed to cope with the optimization of bandwidth. It consists in building a hierarchy of groups of adjacent bands, according to a score to decide which adjacent bands must be merged, before band selection is performed at the different levels of this hierarchy

The Applicability of Short-Wave Infrared (SWIR) Imagery for Archaeological Landscape Classification on Rapa Nui (Easter Island), Chile

High-resolution multispectral imagery provides an effective means for measuring the archaeological record of Rapa Nui. Previous work has suggested that the island’s prehistoric cultivation features known as “lithic mulch gardens” can be identified using near infrared imagery (NIR). Lithic mulching was a laborious but critical strategy for prehistoric populations who relied on cultivating sweet potato and taro in nutrient poor soils for their subsistence. The new WorldView-3 satellite offers researchers access to short-wave infrared (SWIR) bands, imagery that provides additional information about moisture content and mineral composition. While these bands should provide a better means for identifying lithic mulch gardens, this new imagery is currently only available at a lower spatial resolution than NIR images (7.5 m vs. 1.24 m). Here, I evaluate whether these lower-resolution SWIR images can be used for identifying “lithic mulch garden” features despite their resolution difference. Comparing the results of SWIR imagery with that of previous analyses reveals markedly similar classification accuracy despite having the lower spatial resolution. This result suggests that SWIR may provide a new tool for researchers interested in questions of prehistoric land-use that will become increasingly more powerful as greater spatial resolutions become available

The Future of Coral Reefs

This volume contains a series of papers prepared for presentation at the 14th International Coral Reef Symposium, originally planned for July 2020 in Bremen, Germany, but postponed until 2021 (online) and 2022 (in person) because of the COVID-19 pandemic. It contains a series of papers illustrating the breadth of modern studies on coral reefs and the response of the reef science community to the threats that coral reefs now face, above all from climate change. The first group of papers focus on the biology of a selection of reef organisms, ranging from sea fans to coral dwelling crabs. The next group describe studies of coral communities and ecological interactions in regions as diverse as Florida, Kenya, Colombia, and Norway. Further papers describe investigations into the effects of global warming (in the Maldives and in Timor-Leste) and of other impacts (UV blockers, ocean acidification). The final two papers describe the latest applications of satellite and camera technology to the challenge of mapping and monitoring reefs

An Object-Oriented Approach to the Classification of Roofing Materials Using Very High-Resolution Satellite Stereo-Pairs

The availability of multispectral images, with both high spatial and spectral resolution, makes it possible to obtain valuable information about complex urban environment, reducing the need for more expensive surveying techniques. Here, a methodology is tested for the semi-automatic extraction of buildings and the mapping of the main roofing materials over a urban area of approximately 100 km², including the entire city of Bologna (Italy). The methodology follows an object-oriented approach and exploits a limited number of training samples. After a validation based on field inspections and close-range photos acquired by a drone, the final map achieved an overall accuracy of 94% (producer accuracy 79%) regarding the building extraction and of 91% for the classification of the roofing materials. The proposed approach proved to be flexible enough to catch the strong variability of the urban texture in different districts and can be easily reproducible in other contexts, as only satellite imagery is required for the mapping

Vegetation and Tree Species Classification Using Multidate and High-resolution Satellite Imagery and Lidar Data

Remote sensing can play a key role in understanding the makeup of urban forests. This thesis analyzes how high-resolution multispectral imagery, lidar point clouds, and multidate multispectral imagery allow for improved classification of London, Ontario’s urban forest. Chapter 2 uses object-based support vector machine classification (SVM) to classify five types of trees using features derived from Geoeye-1 imagery and lidar data. This results in an overall accuracy of 85.08% when features from both data sources are combined, compared with 77.73% when using only lidar features, and 71.85% when using only imagery features. Chapter 3 makes use of Planetscope and VENuS images from different seasons to classify deciduous trees, conifers, non-tree vegetation, and non-vegetation using SVM. Using multidate Planetscope images increases overall accuracy to 83.11% (8.19 percentage points more than single-date Planetscope classification), while using multidate VENuS images increases accuracy to 72.18% (2.22 percentage points higher than single-date VENuS classification)

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

Performance and Transferability Assessment of Convolutional Neural Network (CNN) Based Building Detection Models for Emergency Response

Remote Sensing data from Earth Observation (EO) is used for a wide variety of applications. Over the last decade, in the event of a natural calamity, the importance of using geo referenced products from satellite and aerial imagery has been on the rise. They play a vital role in helping the first responders by providing valuable information in the form of hazard zone maps that help in relocation of people, in post disaster evaluation to get a better understanding of the impact on the disaster zone and in the rehabilitation and reconstruction of damaged property. In remote sensing-based emergency mapping, there are major limitations during the acquisition and processing of earth observation data. In most cases, satellite data can be acquired only from that set of EO satellites that are in orbit over the hazard zone during the time of the disaster. This can be compensated by deploying sensors on board airplanes and Unmanned Aerial Vehicles (UAVs) like drones for data acquisition. This gives rise to an archive of multi modal data that have different acquisition geometry, radiometry, acquisition conditions and Ground Sampling Distance. This forces the data processing and analysis team to be equipped with methods that can readily handle such versatile data. With the dominance of artificial intelligence in earth observation, this thesis focuses on developing a Convolutional Neural Network (CNN) model that provides a robust performance for detecting exposed buildings when subjected to optical data from different kinds of sensors and platforms. This thesis starts with an approach of training a region-based network to obtain a baseline model, which then is improved gradually by using advanced techniques like data augmentation and fine tuning. A comprehensive performance evaluation is carried out under consideration of different training-testing scenarios. Furthermore, the influence of tile-size on the detection performance is tested. The resultant model after improvements is tested on an independent validation dataset acquired during rapid mapping activation of the Centre for satellite-based crisis information (ZKI) during the floods in Germany, July 2021. Contrary to intuition, the model owning the implementation of augmentation technique on the xView global dataset, shows the best performance for transferability. Due to resource limitation, the pipeline has been trained with a small sliver of the available dataset. The model weights obtained by retraining on the entire dataset with much powerful machines will provide new benchmarks for transferability models in object detection. By combining the resultant exposure with hazard information, we can get a first insight into which areas are likely to be affected in the event of a catastrophe. The importance of this work is that it provides an up-to-date picture of the building stock compared to Open Street Map or cadastre data, at different phases of the disaster

Domain Adaptation in remote sensing: increasing the portability of land-cover classifiers

Among the types of remote sensing acquisitions, optical images are certainly one of the most widely relied upon data sources for Earth observation. They provide detailed measurements of the electromagnetic radiation reflected or emitted by each pixel in the scene. Through a process termed supervised land-cover classification, this allows to automatically yet accurately distinguish objects at the surface of our planet. In this respect, when producing a land-cover map of the surveyed area, the availability of training examples representative of each thematic class is crucial for the success of the classification procedure. However, in real applications, due to several constraints on the sample collection process, labeled pixels are usually scarce. When analyzing an image for which those key samples are unavailable, a viable solution consists in resorting to the ground truth data of other previously acquired images. This option is attractive but several factors such as atmospheric, ground and acquisition conditions can cause radiometric differences between the images, hindering therefore the transfer of knowledge from one image to another. The goal of this Thesis is to supply remote sensing image analysts with suitable processing techniques to ensure a robust portability of the classification models across different images. The ultimate purpose is to map the land-cover classes over large spatial and temporal extents with minimal ground information. To overcome, or simply quantify, the observed shifts in the statistical distribution of the spectra of the materials, we study four approaches issued from the field of machine learning. First, we propose a strategy to intelligently sample the image of interest to collect the labels only in correspondence of the most useful pixels. This iterative routine is based on a constant evaluation of the pertinence to the new image of the initial training data actually belonging to a different image. Second, an approach to reduce the radiometric differences among the images by projecting the respective pixels in a common new data space is presented. We analyze a kernel-based feature extraction framework suited for such problems, showing that, after this relative normalization, the cross-image generalization abilities of a classifier are highly increased. Third, we test a new data-driven measure of distance between probability distributions to assess the distortions caused by differences in the acquisition geometry affecting series of multi-angle images. Also, we gauge the portability of classification models through the sequences. In both exercises, the efficacy of classic physically- and statistically-based normalization methods is discussed. Finally, we explore a new family of approaches based on sparse representations of the samples to reciprocally convert the data space of two images. The projection function bridging the images allows a synthesis of new pixels with more similar characteristics ultimately facilitating the land-cover mapping across images

Remote sensing grass quantity under different grassland management treatments practised in the Southern African rangelands.

Doctor of Philosophy in Environmental Science. University of KwaZulu-Natal, Pietermaritzburg 2016.Abstract available in PDF file