Generative Supervised Classification Using Dirichlet Process Priors.

Choosing the appropriate parameter prior distributions associated to a given Bayesian model is a challenging problem. Conjugate priors can be selected for simplicity motivations. However, conjugate priors can be too restrictive to accurately model the available prior information. This paper studies a new generative supervised classifier which assumes that the parameter prior distributions conditioned on each class are mixtures of Dirichlet processes. The motivations for using mixtures of Dirichlet processes is their known ability to model accurately a large class of probability distributions. A Monte Carlo method allowing one to sample according to the resulting class-conditional posterior distributions is then studied. The parameters appearing in the class-conditional densities can then be estimated using these generated samples (following Bayesian learning). The proposed supervised classifier is applied to the classification of altimetric waveforms backscattered from different surfaces (oceans, ices, forests, and deserts). This classification is a first step before developing tools allowing for the extraction of useful geophysical information from altimetric waveforms backscattered from nonoceanic surfaces

ANALYSIS OF FULL-WAVEFORM LIDAR DATA FOR CLASSIFICATION OF URBAN AREAS

International audienceIn contrast to conventional airborne multi-echo laser scanner systems, full-waveform (FW) lidar systems are able to record the entire emitted and backscattered signal of each laser pulse. Instead of clouds of individual 3D points, FW devices provide connected 1D profiles of the 3D scene, which contain more detailed and additional information about the structure of the illuminated surfaces. This paper is focused on the analysis of FW data in urban areas. The problem of modelling FW lidar signals is first tackled. The standard method assumes the waveform to be the superposition of signal contributions of each scattering object in such a laser beam, which are approximated by Gaussian distributions. This model is suitable in many cases, especially in vegetated terrain. However, since it is not tailored to urban waveforms, the generalized Gaussian model is selected instead here. Then, a pattern recognition method for urban area classification is proposed. A supervised method using Support Vector Machines is performed on the FW point cloud based on the parameters extracted from the post-processing step. Results show that it is possible to partition urban areas in building, vegetation, natural ground and artificial ground regions with high accuracy using only lidar waveforms

Ocean Remote Sensing with Synthetic Aperture Radar

The ocean covers approximately 71% of the Earth’s surface, 90% of the biosphere and contains 97% of Earth’s water. The Synthetic Aperture Radar (SAR) can image the ocean surface in all weather conditions and day or night. SAR remote sensing on ocean and coastal monitoring has become a research hotspot in geoscience and remote sensing. This book—Progress in SAR Oceanography—provides an update of the current state of the science on ocean remote sensing with SAR. Overall, the book presents a variety of marine applications, such as, oceanic surface and internal waves, wind, bathymetry, oil spill, coastline and intertidal zone classification, ship and other man-made objects’ detection, as well as remotely sensed data assimilation. The book is aimed at a wide audience, ranging from graduate students, university teachers and working scientists to policy makers and managers. Efforts have been made to highlight general principles as well as the state-of-the-art technologies in the field of SAR Oceanography

Earth resources, a continuing bibliography with indexes

This bibliography lists 541 reports, articles and other documents introduced into the NASA scientific and technical information system. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

A methodology to produce geographical information for land planning using very-high resolution images

Actualmente, os municípios são obrigados a produzir, no âmbito da elaboração dos instrumentos de gestão territorial, cartografia homologada pela autoridade nacional. O Plano Director Municipal (PDM) tem um período de vigência de 10 anos. Porém, no que diz respeito à cartografia para estes planos, principalmente em municípios onde a pressão urbanística é elevada, esta periodicidade não é compatível com a dinâmica de alteração de uso do solo. Emerge assim, a necessidade de um processo de produção mais eficaz, que permita a obtenção de uma nova cartografia de base e temática mais frequentemente. Em Portugal recorre-se à fotografia aérea como informação de base para a produção de cartografia de grande escala. Por um lado, embora este suporte de informação resulte em mapas bastante rigorosos e detalhados, a sua produção têm custos muito elevados e consomem muito tempo. As imagens de satélite de muito alta-resolução espacial podem constituir uma alternativa, mas sem substituir as fotografias aéreas na produção de cartografia temática, a grande escala. O tema da tese trata assim da satisfação das necessidades municipais em informação geográfica actualizada. Para melhor conhecer o valor e utilidade desta informação, realizou-se um inquérito aos municípios Portugueses. Este passo foi essencial para avaliar a pertinência e a utilidade da introdução de imagens de satélite de muito alta-resolução espacial na cadeia de procedimentos de actualização de alguns temas, quer na cartografia de base quer na cartografia temática. A abordagem proposta para solução do problema identificado baseia-se no uso de imagens de satélite e outros dados digitais em ambiente de Sistemas de Informação Geográfica. A experimentação teve como objectivo a extracção automática de elementos de interesse municipal a partir de imagens de muito alta-resolução espacial (fotografias aéreas ortorectificadas, imagem QuickBird, e imagem IKONOS), bem como de dados altimétricos (dados LiDAR). Avaliaram-se as potencialidades da informação geográfica extraídas das imagens para fins cartográficos e analíticos. Desenvolveram-se quatro casos de estudo que reflectem diferentes usos para os dados geográficos a nível municipal, e que traduzem aplicações com exigências diferentes. No primeiro caso de estudo, propõe-se uma metodologia para actualização periódica de cartografia a grande escala, que faz uso de fotografias aéreas vi ortorectificadas na área da Alta de Lisboa. Esta é uma aplicação quantitativa onde as qualidades posicionais e geométricas dos elementos extraídos são mais exigentes. No segundo caso de estudo, criou-se um sistema de alarme para áreas potencialmente alteradas, com recurso a uma imagem QuickBird e dados LiDAR, no Bairro da Madre de Deus, com objectivo de auxiliar a actualização de cartografia de grande escala. No terceiro caso de estudo avaliou-se o potencial solar de topos de edifícios nas Avenidas Novas, com recurso a dados LiDAR. No quarto caso de estudo, propõe-se uma série de indicadores municipais de monitorização territorial, obtidos pelo processamento de uma imagem IKONOS que cobre toda a área do concelho de Lisboa. Esta é uma aplicação com fins analíticos onde a qualidade temática da extracção é mais relevante.Currently, the Portuguese municipalities are required to produce homologated cartography, under the Territorial Management Instruments framework. The Municipal Master Plan (PDM) has to be revised every 10 years, as well as the topographic and thematic maps that describe the municipal territory. However, this period is inadequate for representing counties where urban pressure is high, and where the changes in the land use are very dynamic. Consequently, emerges the need for a more efficient mapping process, allowing obtaining recent geographic information more often. Several countries, including Portugal, continue to use aerial photography for large-scale mapping. Although this data enables highly accurate maps, its acquisition and visual interpretation are very costly and time consuming. Very-High Resolution (VHR) satellite imagery can be an alternative data source, without replacing the aerial images, for producing large-scale thematic cartography. The focus of the thesis is the demand for updated geographic information in the land planning process. To better understand the value and usefulness of this information, a survey of all Portuguese municipalities was carried out. This step was essential for assessing the relevance and usefulness of the introduction of VHR satellite imagery in the chain of procedures for updating land information. The proposed methodology is based on the use of VHR satellite imagery, and other digital data, in a Geographic Information Systems (GIS) environment. Different algorithms for feature extraction that take into account the variation in texture, color and shape of objects in the image, were tested. The trials aimed for automatic extraction of features of municipal interest, based on aerial and satellite high-resolution (orthophotos, QuickBird and IKONOS imagery) as well as elevation data (altimetric information and LiDAR data). To evaluate the potential of geographic information extracted from VHR images, two areas of application were identified: mapping and analytical purposes. Four case studies that reflect different uses of geographic data at the municipal level, with different accuracy requirements, were considered. The first case study presents a methodology for periodic updating of large-scale maps based on orthophotos, in the area of Alta de Lisboa. This is a situation where the positional and geometric accuracy of the extracted information are more demanding, since technical mapping standards must be complied. In the second case study, an alarm system that indicates the location of potential changes in building areas, using a QuickBird image and LiDAR data, was developed for the area of Bairro da Madre de Deus. The goal of the system is to assist the updating of large scale mapping, providing a layer that can be used by the municipal technicians as the basis for manual editing. In the third case study, the analysis of the most suitable roof-tops for installing solar systems, using LiDAR data, was performed in the area of Avenidas Novas. A set of urban environment indicators obtained from VHR imagery is presented. The concept is demonstrated for the entire city of Lisbon, through IKONOS imagery processing. In this analytical application, the positional quality issue of extraction is less relevant.GEOSAT – Methodologies to extract large scale GEOgraphical information from very high resolution SATellite images (PTDC/GEO/64826/2006), e-GEO – Centro de Estudos de Geografia e Planeamento Regional, da Faculdade de Ciências Sociais e Humanas, no quadro do Grupo de Investigação Modelação Geográfica, Cidades e Ordenamento do Territóri

Amélioration des estimations hydrométriques dérivées des données altimétriques satellitaires acquises sur des étendues d’eau continentales soumises à l’englacement

Les eaux douces continentales constituent l’une des composantes principales du cycle de l’eau. Elles assurent sa continuité à travers des échanges de flux d’eau et d’énergie avec ses différentes composantes. De nombreux plans d’eau douce (lacs, rivières, réservoirs, etc.) se retrouvent dans les régions situées dans les hautes latitudes nord, où la cryosphère est dominante. L’une des particularités de ces plans d’eau est la congélation partielle ou complète pendant les saisons froides. De plus, ils ont une grande sensibilité aux changements climatiques. En effet, les variations spatio-temporelles du climat aux échelles régionales et locales affectent grandement l’hydrologie de ces plans d’eau en termes de niveau d’eau et de débit. D’où l’intérêt de disposer d’outils simples et efficaces pour surveiller et gérer ces ressources. L’inaccessibilité aux plans d’eau isolés et l’effet de la glace sur la qualité des mesures des niveaux d’eau à l’échelle des stations limnimétriques rendent la surveillance de la variation des niveaux d’eau difficiles. Compte tenu de sa couverture spatio-temporelle, de sa période de répétitivité, et des bandes de fréquence utilisées, l’altimétrie radar par satellite pourrait être une meilleure alternative pour surmonter les limites liées aux mesures in situ. Cependant, la présence de cibles hétérogènes, comme les couverts de glace, présente un défi majeur pour exploiter les données des niveaux d’eau dérivées de la technologie par satellite altimétrique au-dessus des plans d’eau couverts de glace. Cette étude a pour ultime objectif d’améliorer les estimations des niveaux d’eau dérivées de l’altimétrie radar par satellite sur des étendues d’eau continentales couvertes de glace. L’étude s’applique à étudier le potentiel de deux satellites altimétriques, Jason-2 et SARAL/Altika, possédant des caractéristiques technologiques différentes, à suivre les variations des niveaux d’eau des étendues d’eau soumises à l’englacement sur le territoire canadien. Le premier objectif spécifique de cette étude concerne l’analyse de la capacité des algorithmes de retraitements utilisés par les missions Jason-2 et SARAL/Altika à estimer les niveaux d’eau sur vingt étendues d’eau couvertes de glace au Canada. Cette analyse est effectuée sur les produits dérivés des algorithmes de retraitement et sur les mesures in situ pendant deux périodes : la période de recouvrement des satellites Jason-2 et SARAL/Altika, comprise entre 2008 et 2016, et les périodes des variations saisonnières de l’état de surface. Les résultats montrent que pour Jason-2, c’est l’algorithme de seuillage ICE-1 qui fournit les meilleures estimations de niveau d’eau, avec des erreurs RMSE non biaisées (unRMSE) ≤ 0,3 m et des r ≥ 0,8 pour 90 % des étendues d’eau. Pour ce qui est de SARAL/Altika, la majorité des algorithmes de retraitement utilisés donnent des résultats très comparables aux observations in situ, démontrant les bonnes performances de la technologie SARAL. Cependant, les algorithmes de retraitement utilisés par les deux satellites Jason-2 et SARAL/Altika fournissent des précisions faibles pendant les périodes marquées par le mélange eau-glace, c’est-à-dire les périodes de gel et de dégel. Le deuxième objectif spécifique est d’améliorer les estimations des niveaux d’eau issues du satellite Jason-2 pendant les périodes de gel et de dégel. Une approche de détection automatique est proposée afin de discriminer les points de mesure de l’eau libre, de la glace pure et de la glace partielle sur quatre plans d’eau couverts de glace : le Grand Lac des Esclaves, le lac Athabasca, le lac Winnipeg, et le lac des Bois. Cette approche se base sur l’intégration des données actives et passives du satellite Jason-2 dans un processus de clustering afin de définir les clusters correspondant à chaque état de surface. L’application du seuil de détection du cluster de l’eau libre a permis d'améliorer la qualité des mesures de niveau d'eau pendant les périodes de gel et de dégel. Les résultats montrent que le coefficient de corrélation r est amélioré d’environ 0,8 à plus de 0,9 avec des biais inférieurs à 20 cm. Le troisième objectif spécifique évalue le potentiel de l’approche de détection automatique des points de mesures développé dans l’objectif 2, avec les données du satellite SARAL/Altika. Dans cette partie, les données actives et passives dérivées du satellite SARAL/Altika ont été exploitées pour concevoir les seuils de discrimination de chaque état de surface (eau libre, glace pure, glace partielle de gel et dégel) sur les mêmes quatre plans d’eau étudiés. L’application du seuil de l’eau libre offre une amélioration de la qualité des mesures de niveau de l’eau avec une amélioration des corrélations r d’environ 0,8 à plus de 0,92 avec des biais inférieurs à 10 cm. Le quatrième objectif spécifique met en place une approche de classification des formes d’onde selon la nature et l’état de surface pendant les périodes de gel et de dégel pour les satellites altimétriques Jason-2 et SARAL/Altika. Le site d’étude considéré pour le développement de cette approche est le Grand Lac des Esclaves. Un processus de classification non supervisée basé sur les paramètres des formes d’onde et les résultats des interprétations des données altimétriques et radiométriques sur l’état de surface a été utilisé avant de développer l’approche de classification supervisée des formes d’onde pour Jason-2 et SARAL/Altika, nommée le modèle entrainé de classification - Classification Trained Model (CTM). Les modèles supervisés du K-plus proche voisin (KNN, K-Nearest Neighbour) et de machine à vecteurs de support (SVM, Support Vector Machine) ont été évalués pour cette conception. Le modèle basé sur l’approche SVM a produit les meilleurs résultats, présentant une précision globale (Overall Accuracy) de l’ordre de 92 % avec Jason-2 et de 98 % avec SARAL/Altika. Ce modèle développé est utilisé pour classifier l’ensemble des formes d’onde en fonction de l’état de surface du plan d’eau étudié. Les résultats ont été superposés à des produits Moderate Resolution Imaging Spectroradiometer (MODIS) pour une évaluation qualitative et semi-quantitative.Abstract : The continental freshwater is one of the main components of the water cycle. These resouces ensure its continuity through the exchange of water and energy fluxes with the different components of the water cycle. Most of the continental water bodies (lakes, rivers, reservoirs, etc.) are in the northern high latitudes, dominated by the cryosphere. These water bodies froze completely or partly during cold seasons. In addition, they have a high sensitivity to climate change. Climate variations at the local and global scales may affect the hydrological regime (water level and flow) of these water bodies. Hence the interest in having a simple and efficient tools to monitor changes of these resources. The gauging stations could not provide good measurements of water level due to the limited accessibility of isolated water bodies, and the potential contamination of measured data by ice. Satellite radar altimetry appears as a good alternative to overcome these limitations given its spatiotemporal coverage, its ground track repetitivity period, and the frequency bands used. However, the presence of heterogeneous targets within the altimeter footprint, such as ice cover, remains a major challenge to estimate water levels over ice-covered water bodies. The aim of this study is to improve the estimations of water levels obtained from spatial radar altimetry over ice-covered water bodies. This study investigates the potential of the two satellites altimetry Jason-2 and SARAL/Altika with different characteristics to monitor water-level changes over ice-covered water bodies in the Canadian territory. The first objective of this study is to analyze the potential of Jason-2 and SARAL/Altika retracking algorithms to retrieve water levels from altimeter measurements acquired over 20 ice-covered water bodies across Canada. In this analysis, products derived from retracking algorithms were compared with in situ measurements during two periods: (1) the time series considered for each satellite (2008–2016 for Jason-2, and 2013–2016 for SARAL/Altika); and (2) the freeze-thaw periods included in each time series. The results showed that retracking ICE-1 (used with Jason-2 data) provided better water level accuracy for 90% of the studied water bodies (r ≥ 0.8, unbiased RMSE ≤ 0.3 m). All the retracking algorithms used by SARAL/Altika provided results that are comparable to in situ observations, thus denoting the good performance of the SARAL technology. However, all retracking algorithms used by Jason-2 and SARAL/Altika provide low accuracy during freeze-up and thaw periods. The second objective attempts to improve the measurements of water levels obtained by Jason-2 data during freeze and thaw periods. Here, an automatic approach is proposed to identify the Jason-2 altimetry measurements corresponding to open water, ice, and transition (water ice) over four Canadian lakes: Great Slave Lake, Lake Athabasca, Lake Winnipeg, and Lake of the Woods. This approach is based on the integration of backscatter coefficients and peakiness at Ku-band and brightness temperature observations obtained from Jason-2 data in a clustering process to define the clusters and threshold of each surface state. The use of open water threshold improves the quality of water-level estimation over the four lakes during freeze-up and thaw periods. The results show that the coefficient of correlation (r) increased in average from about 0.8 without the use of the thresholds to more than 0.90, with unbiased RMSE errors less than 20 cm. The third objective evaluates the efficiency of the automatic approach proposed in the second objective, with SARAL/Altika data. In this section, active and passive observations derived from SARAL/Altika data were used to design the thresholds of each state surface (open water, pure ice, ice freeze-up, and ice break-up) over the same four studied water bodies. The application of open water threshold improved the quality of water levels measurements from r ~ 0.8 to r more than 0.92 with unbiased RMSE less than 10 cm. The fourth objective proposes a new approach for classifying waveforms data derived from Jason-2 and SARAL/Altika satellite missions during freeze-up and thaw periods based on the surface state over ice-covered water bodies. The considered study area for the development of this approach is Great Slave Lake. An unsupervised classification process based on waveform parameters and the results of interpretations of active and passive data was used before developing the supervised classification approach for Jason-2 and SARAL/Altika, named Classification Trained Model (CTM). K-nearest neighbor (KNN) and support vector machine (SVM) models were evaluated for this concept. The SVM-based model provided the best results (accuracy of 92% with Jason-2, and 98% with SARAL/Altika). It was used to classify all waveforms of the studied water body. Results were superimposed to MODIS products for qualitative visual and semi-quantitative assessments

Earth Resources. A continuing bibliography with indexes, issue 34, July 1982

This bibliography lists 567 reports, articles, and other documents introduced into the NASA Scientific and Technical Information System between April 1, and June 30, 1982. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Ocean remote sensing techniques and applications: a review (Part II)

As discussed in the first part of this review paper, Remote Sensing (RS) systems are great tools to study various oceanographic parameters. Part I of this study described different passive and active RS systems and six applications of RS in ocean studies, including Ocean Surface Wind (OSW), Ocean Surface Current (OSC), Ocean Wave Height (OWH), Sea Level (SL), Ocean Tide (OT), and Ship Detection (SD). In Part II, the remaining nine important applications of RS systems for ocean environments, including Iceberg, Sea Ice (SI), Sea Surface temperature (SST), Ocean Surface Salinity (OSS), Ocean Color (OC), Ocean Chlorophyll (OCh), Ocean Oil Spill (OOS), Underwater Ocean, and Fishery are comprehensively reviewed and discussed. For each application, the applicable RS systems, their advantages and disadvantages, various RS and Machine Learning (ML) techniques, and several case studies are discussed.Peer ReviewedPostprint (published version