A Global Human Settlement Layer from optical high resolution imagery - Concept and first results

A general framework for processing of high and very-high resolution imagery for creating a Global Human Settlement Layer (GHSL) is presented together with a discussion on the results of the first operational test of the production workflow. The test involved the mapping of 24.3 millions of square kilometres of the Earth surface spread over four continents, corresponding to an estimated population of 1.3 billion of people in 2010. The resolution of the input image data ranges from 0.5 to 10 meters, collected by a heterogeneous set of platforms including satellite SPOT (2 and 5), CBERS-2B, RapidEye (2 and 4), WorldView (1 and 2), GeoEye-1, QuickBird-2, Ikonos-2, and airborne sensors. Several imaging modes were tested including panchromatic, multispectral and pan-sharpened images. A new fully automatic image information extraction, generalization and mosaic workflow is presented that is based on multiscale textural and morphological image features extraction. New image feature compression and optimization are introduced, together with new learning and classification techniques allowing for the processing of HR/VHR image data using low-resolution thematic layers as reference. A new systematic approach for quality control and validation allowing global spatial and thematic consistency checking is proposed and applied. The quality of the results are discussed by sensor, by band, by resolution, and eco-regions. Critical points, lessons learned and next steps are highlighted.JRC.G.2-Global security and crisis managemen

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

Efficient Algorithms for Large-Scale Image Analysis

This work develops highly efficient algorithms for analyzing large images. Applications include object-based change detection and screening. The algorithms are 10-100 times as fast as existing software, sometimes even outperforming FGPA/GPU hardware, because they are designed to suit the computer architecture. This thesis describes the implementation details and the underlying algorithm engineering methodology, so that both may also be applied to other applications

Multisource Data Integration in Remote Sensing

Papers presented at the workshop on Multisource Data Integration in Remote Sensing are compiled. The full text of these papers is included. New instruments and new sensors are discussed that can provide us with a large variety of new views of the real world. This huge amount of data has to be combined and integrated in a (computer-) model of this world. Multiple sources may give complimentary views of the world - consistent observations from different (and independent) data sources support each other and increase their credibility, while contradictions may be caused by noise, errors during processing, or misinterpretations, and can be identified as such. As a consequence, integration results are very reliable and represent a valid source of information for any geographical information system

Multisource and Multitemporal Data Fusion in Remote Sensing

The sharp and recent increase in the availability of data captured by different sensors combined with their considerably heterogeneous natures poses a serious challenge for the effective and efficient processing of remotely sensed data. Such an increase in remote sensing and ancillary datasets, however, opens up the possibility of utilizing multimodal datasets in a joint manner to further improve the performance of the processing approaches with respect to the application at hand. Multisource data fusion has, therefore, received enormous attention from researchers worldwide for a wide variety of applications. Moreover, thanks to the revisit capability of several spaceborne sensors, the integration of the temporal information with the spatial and/or spectral/backscattering information of the remotely sensed data is possible and helps to move from a representation of 2D/3D data to 4D data structures, where the time variable adds new information as well as challenges for the information extraction algorithms. There are a huge number of research works dedicated to multisource and multitemporal data fusion, but the methods for the fusion of different modalities have expanded in different paths according to each research community. This paper brings together the advances of multisource and multitemporal data fusion approaches with respect to different research communities and provides a thorough and discipline-specific starting point for researchers at different levels (i.e., students, researchers, and senior researchers) willing to conduct novel investigations on this challenging topic by supplying sufficient detail and references

Fotogrametría de rango cercano aplicada a la Ingeniería Agroforestal

Tesis por compendio de publicaciones[EN]Since the late twentieth century, Geotechnologies are being applied in different research lines in Agroforestry Engineering aimed at advancing in the modeling of biophysical parameters in order to improve the productivity. In this study, low-cost and close range photogrammetry has been used in different agroforestry scenarios to solve identified gaps in the results and improve procedures and technology hitherto practiced in this field. Photogrammetry offers the advantage of being a non-destructive and non-invasive technique, never changing physical properties of the studied element, providing rigor and completeness to the captured information. In this PhD dissertation, the following contributions are presented divided into three research papers: • A methodological proposal to acquire georeferenced multispectral data of high spatial resolution using a low-cost manned aerial platform, to monitor and sustainably manage extensive áreas of crops. The vicarious calibration is exposed as radiometric calibration method of the multispectral sensor embarked on a paraglider. Low-cost surfaces are performed as control coverages. • The development of a method able to determine crop productivity under field conditions, from the combination of close range photogrammetry and computer vision, providing a constant operational improvement and a proactive management in the crop monitoring. An innovate methodology in the sector is proposed, ensuring flexibility and simplicity in the data collection by non-invasive technologies, automation in processing and quality results with low associated cost. • A low cost, efficient and accurate methodology to obtain Digital Height Models of vegatal cover intended for forestry inventories by integrating public data from LiDAR into photogrammetric point clouds coming from low cost flights. This methodology includes the potentiality of LiDAR to register ground points in areas with high density of vegetation and the better spatial, radiometric and temporal resolution from photogrammetry for the top of vegetal covers.[ES]Desde finales del siglo XX se están aplicando Geotecnologías en diferentes líneas de investigación en Ingeniería Agroforestal orientadas a avanzar en la modelización de parámetros biofísicos con el propósito de mejorar la productividad. En este estudio se ha empleado fotogrametría de bajo coste y rango cercano en distintos escenarios agroforestales para solventar carencias detectadas en los resultados obtenidos y mejorar los procedimientos y la tecnología hasta ahora usados en este campo. La fotogrametría ofrece como ventaja el ser una técnica no invasiva y no destructiva, por lo que no altera en ningún momento las propiedades físicas del elemento estudiado, dotando de rigor y exhaustividad a la información capturada. En esta Tesis Doctoral se presentan las siguientes contribuciones, divididas en tres artículos de investigación: • Una propuesta metodológica de adquisición de datos multiespectrales georreferenciados de alta resolución espacial mediante una plataforma aérea tripulada de bajo coste, para monitorizar y gestionar sosteniblemente amplias extensiones de cultivos. Se expone la calibración vicaria como método de calibración radiométrico del sensor multiespectral embarcado en un paramotor empleando como coberturas de control superficies de bajo coste. • El desarrollo de un método capaz de determinar la productividad del cultivo en condiciones de campo, a partir de la combinación de fotogrametría de rango cercano y visión computacional, facilitando una mejora operativa constante así como una gestión proactiva en la monitorización del cultivo. Se propone una metodología totalmente novedosa en el sector, garantizando flexibilidad y sencillez en la toma de datos mediante tecnologías no invasivas, automatismo en el procesado, calidad en los resultados y un bajo coste asociado. • Una metodología de bajo coste, eficiente y precisa para la obtención de Modelos Digitales de Altura de Cubierta Vegetal destinados al inventario forestal mediante la integración de datos públicos procedentes del LiDAR en las nubes de puntos fotogramétricas obtenidas con un vuelo de bajo coste. Esta metodología engloba la potencialidad del LiDAR para registrar el terreno en zonas con alta densidad de vegetación y una mejor resolución espacial, radiométrica y temporal procedente de la fotogrametría para la parte superior de las cubiertas vegetales

Crop Disease Detection Using Remote Sensing Image Analysis

Pest and crop disease threats are often estimated by complex changes in crops and the applied agricultural practices that result mainly from the increasing food demand and climate change at global level. In an attempt to explore high-end and sustainable solutions for both pest and crop disease management, remote sensing technologies have been employed, taking advantages of possible changes deriving from relative alterations in the metabolic activity of infected crops which in turn are highly associated to crop spectral reflectance properties. Recent developments applied to high resolution data acquired with remote sensing tools, offer an additional tool which is the opportunity of mapping the infected field areas in the form of patchy land areas or those areas that are susceptible to diseases. This makes easier the discrimination between healthy and diseased crops, providing an additional tool to crop monitoring. The current book brings together recent research work comprising of innovative applications that involve novel remote sensing approaches and their applications oriented to crop disease detection. The book provides an in-depth view of the developments in remote sensing and explores its potential to assess health status in crops

Recent Advances in Image Restoration with Applications to Real World Problems

In the past few decades, imaging hardware has improved tremendously in terms of resolution, making widespread usage of images in many diverse applications on Earth and planetary missions. However, practical issues associated with image acquisition are still affecting image quality. Some of these issues such as blurring, measurement noise, mosaicing artifacts, low spatial or spectral resolution, etc. can seriously affect the accuracy of the aforementioned applications. This book intends to provide the reader with a glimpse of the latest developments and recent advances in image restoration, which includes image super-resolution, image fusion to enhance spatial, spectral resolution, and temporal resolutions, and the generation of synthetic images using deep learning techniques. Some practical applications are also included