The Experts below are selected from a list of 279 Experts worldwide ranked by ideXlab platform

Richard Lucas - One of the best experts on this subject based on the ideXlab platform.

  • remap an online Remote Sensing Application for land cover classification and monitoring
    Methods in Ecology and Evolution, 2018
    Co-Authors: Nicholas J Murray, David A Keith, Daniel Simpson, John H Wilshire, Richard Lucas
    Abstract:

    Recent assessments of progress towards global conservation targets have revealed a paucity of indicators suitable for assessing the changing state of ecosystems. Moreover, land managers and planners are often unable to gain timely access to the maps they need to support their routine decision-making. This deficiency is partly due to a lack of suitable data on ecosystem change, driven mostly by the considerable technical expertise needed to develop ecosystem maps from Remote Sensing data. We have developed a free and open-access online Remote Sensing and environmental modelling Application, the Remote Ecosystem Monitoring and Assessment Pipeline (Remap; https://remap-app.org), that enables volunteers, managers and scientists with little or no experience in Remote Sensing to generate classifications (maps) of land cover and land use change over time. Remap utilizes the geospatial data storage and analysis capacity of Google Earth Engine and requires only spatially resolved training data that define map classes of interest (e.g. ecosystem types). The training data, which can be uploaded or annotated interactively within Remap, are used in a random forest classification of up to 13 publicly available predictor datasets to assign all pixels in a focal region to map classes. Predictor datasets available in Remap represent topographic (e.g. slope, elevation), spectral (archival Landsat image composites) and climatic variables (precipitation, temperature) that are relevant to the distribution of ecosystems and land cover classes. The ability of Remap to develop and export high-quality classified maps in a very short (<10 min) time frame represents a considerable advance towards globally accessible and free Application of Remote Sensing technology. By enabling access to data and simplifying Remote Sensing classifications, Remap can catalyse the monitoring of land use and change to support environmental conservation, including developing inventories of biodiversity, identifying hotspots of ecosystem diversity, ecosystem-based spatial conservation planning, mapping ecosystem loss at local scales and supporting environmental education initiatives.

  • remap an online Remote Sensing Application for land cover classification and monitoring
    bioRxiv, 2018
    Co-Authors: Nicholas J Murray, David A Keith, Daniel Simpson, John H Wilshire, Richard Lucas
    Abstract:

    Recent assessments of progress towards global conservation targets have revealed a paucity of indicators suitable for assessing the changing state of ecosystems. Moreover, land managers and planners are often unable to gain timely access to maps they need to support their routine decision-making. This deficiency is partly due to a lack of suitable data on ecosystem change, driven mostly by the considerable technical expertise needed to make ecosystem maps from Remote Sensing data. We have developed a free and open-access online Remote Sensing and environmental modelling Application, REMAP (the Remote ecosystem monitoring and assessment pipeline; https://remap-app.org) that enables volunteers, managers, and scientists with little or no experience in Remote Sensing to develop high-resolution classified maps of land cover and land use change over time. REMAP utilizes the geospatial data storage and analysis capacity of the Google Earth Engine, and requires only spatially resolved training data that define map classes of interest (e.g., ecosystem types). The training data, which can be uploaded or annotated interactively within REMAP, are used in a random forest classification of up to 13 publicly available predictor datasets to assign all pixels in a focal region to map classes. Predictor datasets available in REMAP represent topographic (e.g. slope, elevation), spectral (Landsat Archive image composites) and climatic variables (precipitation, temperature) that can inform on the distribution of ecosystems and land cover classes. The ability of REMAP to develop and export high-quality classified maps in a very short (

Guoyin Cai - One of the best experts on this subject based on the ideXlab platform.

  • International Conference on Computational Science (1) - A Remote Sensing Application workflow and its implementation in Remote Sensing service grid node
    Computational Science – ICCS 2006, 2006
    Co-Authors: Ying Luo, Yong Xue, Jianping Guo, Wei Wan, Lei Zheng, Guoyin Cai, Shaobo Zhong, Zhengfang Wang
    Abstract:

    In this article we describe a Remote Sensing Application workflow in building a Remote Sensing Information Analysis and Service Grid Node at Institute of Remote Sensing Applications based on the Condor platform. The goal of the Node is to make good use of physically distributed resources in the field of Remote Sensing science such as data, models and algorithms, and computing resource left unused on Internet. Implementing it we use workflow technology to manage the node, control resources, and make traditional algorithms as a Grid service. We use web service technology to communicate with Spatial Information Grid (SIG) and other Grid systems. We use JSP technology to provide an independent portal. Finally, the current status of this ongoing work is described.

  • International Conference on Computational Science (3) - Java-Based grid service spread and implementation in Remote Sensing Applications
    Lecture Notes in Computer Science, 2005
    Co-Authors: Yanguang Wang, Ying Luo, Yong Xue, Shaobo Zhong, Jianqin Wang, Jiakui Tang, Guoyin Cai
    Abstract:

    Remote Sensing Applications often concern very large volumes of spatio-temporal data, the emerging Grid computing technologies bring an effective solution to this problem. The Open Grid Services Architecture (OGSA) treats Grid as the aggregate of Grid service, which is extension of Web Service. It defines standard mechanisms for creating, naming, and discovering transient Grid service instances; provides location transparency and multiple protocol bindings for service instances; and supports integration with underlying native platform facilities. It is not effective used in data-intensive computing such as Remote Sensing Applications because its foundation, Web Service, is not efficient in scientific computing. How to increase the efficiency of the grid services for a scientific computing? This paper proposes a mechanism Grid service spread (GSS), which dynamically replant a Grid service from a Grid node to the others. We have more computers to provide the same function, so less time can be spent completing a problem than original Grid system. This paper also provides the solution how to adept the service duplicate for the destination node’s Grid environment; how each service duplicate communicates with each other; how to manage the lifecycle of services spread etc. The efficiency of this solution through a Remote Sensing Application of NDVI computing is demonstrated. It shows that this method is more efficient for processing huge amount of Remotely sensed data.

M Manteiga - One of the best experts on this subject based on the ideXlab platform.

Ying Luo - One of the best experts on this subject based on the ideXlab platform.

  • International Conference on Computational Science (1) - A Remote Sensing Application workflow and its implementation in Remote Sensing service grid node
    Computational Science – ICCS 2006, 2006
    Co-Authors: Ying Luo, Yong Xue, Jianping Guo, Wei Wan, Lei Zheng, Guoyin Cai, Shaobo Zhong, Zhengfang Wang
    Abstract:

    In this article we describe a Remote Sensing Application workflow in building a Remote Sensing Information Analysis and Service Grid Node at Institute of Remote Sensing Applications based on the Condor platform. The goal of the Node is to make good use of physically distributed resources in the field of Remote Sensing science such as data, models and algorithms, and computing resource left unused on Internet. Implementing it we use workflow technology to manage the node, control resources, and make traditional algorithms as a Grid service. We use web service technology to communicate with Spatial Information Grid (SIG) and other Grid systems. We use JSP technology to provide an independent portal. Finally, the current status of this ongoing work is described.

  • International Conference on Computational Science (3) - Java-Based grid service spread and implementation in Remote Sensing Applications
    Lecture Notes in Computer Science, 2005
    Co-Authors: Yanguang Wang, Ying Luo, Yong Xue, Shaobo Zhong, Jianqin Wang, Jiakui Tang, Guoyin Cai
    Abstract:

    Remote Sensing Applications often concern very large volumes of spatio-temporal data, the emerging Grid computing technologies bring an effective solution to this problem. The Open Grid Services Architecture (OGSA) treats Grid as the aggregate of Grid service, which is extension of Web Service. It defines standard mechanisms for creating, naming, and discovering transient Grid service instances; provides location transparency and multiple protocol bindings for service instances; and supports integration with underlying native platform facilities. It is not effective used in data-intensive computing such as Remote Sensing Applications because its foundation, Web Service, is not efficient in scientific computing. How to increase the efficiency of the grid services for a scientific computing? This paper proposes a mechanism Grid service spread (GSS), which dynamically replant a Grid service from a Grid node to the others. We have more computers to provide the same function, so less time can be spent completing a problem than original Grid system. This paper also provides the solution how to adept the service duplicate for the destination node’s Grid environment; how each service duplicate communicates with each other; how to manage the lifecycle of services spread etc. The efficiency of this solution through a Remote Sensing Application of NDVI computing is demonstrated. It shows that this method is more efficient for processing huge amount of Remotely sensed data.

Jocelyn Chanussot - One of the best experts on this subject based on the ideXlab platform.

  • Chapter 3.1 - Applications in Remote Sensing—natural landscapes
    2020
    Co-Authors: Touria Bajjouk, Florian De Boissieu, Jocelyn Chanussot, Sylvain Dout, Marie Dumont, Jean-baptiste Féret, Théo Masson, Audrey Minghelli, Pascal Mouquet, Frédéric Schmidt
    Abstract:

    This chapter gives an overview on the use of hyperspectral imagery in Remote Sensing. Specifically, four thematic Applications dealing with the characterization of natural landscapes are presented, giving the reader a glimpse of the role that hyperspectral imaging technologies play in environmental monitoring. Namely, Applications related to planetary sciences, coastal areas, cryosphere, and vegetation are reported. For each Remote Sensing Application considered in this chapter, some context is recalled allowing then to introduce a real case study and finally to present some open challenges.

  • Fusion of MultiSpectral and Panchromatic Images Based on Morphological Operators
    IEEE Transactions on Image Processing, 2016
    Co-Authors: Rocco Restaino, Gemine Vivone, Mauro Dalla Mura, Jocelyn Chanussot
    Abstract:

    Nonlinear decomposition schemes constitute an alternative to classical approaches for facing the problem of data fusion. In this paper we discuss the Application of this methodology to a popular Remote Sensing Application called pansharpening, which consists in the fusion of a low resolution multispectral image and a high resolution panchromatic image. We design a complete pansharpening scheme based on the use of morphological half gradients operators and demonstrate the suitability of this algorithm through the comparison with state of the art approaches. Four datasets acquired by the Pleiades, Worldview-2, Ikonos and Geoeye-1 satellites are employed for the performance assessment, testifying the effectiveness of the proposed approach in producing top-class images with a setting independent of the specific sensor.

  • Super-resolution: an efficient method to improve spatial resolution of hyperspectral images
    2010
    Co-Authors: Alberto Villa, Jocelyn Chanussot, Jon Atli Benediktsson, M. Ulfarsson, Christian Jutten
    Abstract:

    Hyperspectral imaging is a continuously growing area of Remote Sensing Application. The wide spectral range, providing a very high spectral resolution, allows to detect and classify surfaces and chemical elements of the observed image. The main problem of hyperspectral data is that the high spectral resolution is usually complementary to the spatial one, which can vary from a few to tens of meters. Many factors, such as imperfect imaging optics, atmospheric scattering, secondary illumination effects and sensor noise cause a degradation of the acquired image quality, making the spatial resolution one of the most expensive and hardest to improve in imaging systems. In this work, a novel method, based on the use of source separation technique and a spatial regularization step by simulated annealing is proposed to improve the spatial resolution of cover classification maps. Experiments have been carried out on both synthetic and real hyperspectral data and show the effectiveness of the proposed method.