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IEEE Geoscience and Remote Sensing Magazine

Issue 2 • Date June 2016

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Displaying Results 1 - 22 of 22
  • [Front cover]

    Publication Year: 2016, Page(s): C1
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  • Call for Papers IEEE Geoscience and Remote Sensing Magazine

    Publication Year: 2016, Page(s):C2 - c2
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  • Table of contents

    Publication Year: 2016, Page(s):1 - 2
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  • Staff Listing

    Publication Year: 2016, Page(s): 2
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  • A Special Issue Before IGARSS 2016 [From the Editor]

    Publication Year: 2016, Page(s): 3
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  • Advancing the Understanding of Our Living Planet [President's Message]

    Publication Year: 2016, Page(s):4 - 7
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  • A Special Issue on Advances in Machine Learning for Remote Sensing and Geosciences [From the Guest Editors]

    Publication Year: 2016, Page(s):5 - 7
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  • Monitoring Land-Cover Changes: A Machine-Learning Perspective

    Publication Year: 2016, Page(s):8 - 21
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2904 KB)

    Monitoring land-cover changes is of prime importance for the effective planning and management of critical, natural and man-made resources. The growing availability of remote sensing data provides ample opportunities for monitoring land-cover changes on a global scale using machine-learning techniques. However, remote sensing data sets exhibit unique domain-specific properties that limit the usefu... View full abstract»

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  • Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art

    Publication Year: 2016, Page(s):22 - 40
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3617 KB)

    Deep-learning (DL) algorithms, which learn the representative and discriminative features in a hierarchical manner from the data, have recently become a hotspot in the machine-learning area and have been introduced into the geoscience and remote sensing (RS) community for RS big data analysis. Considering the low-level features (e.g., spectral and texture) as the bottom level, the output feature r... View full abstract»

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  • Domain Adaptation for the Classification of Remote Sensing Data: An Overview of Recent Advances

    Publication Year: 2016, Page(s):41 - 57
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2939 KB)

    The success of the supervised classification of remotely sensed images acquired over large geographical areas or at short time intervals strongly depends on the representativity of the samples used to train the classification algorithm and to define the model. When training samples are collected from an image or a spatial region that is different from the one used for mapping, spectral shifts betw... View full abstract»

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  • A Survey on Gaussian Processes for Earth-Observation Data Analysis: A Comprehensive Investigation

    Publication Year: 2016, Page(s):58 - 78
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (4999 KB) |  Multimedia Media

    Gaussian processes (GPs) have experienced tremendous success in biogeophysical parameter retrieval in the last few years. GPs constitute a solid Bayesian framework to consistently formulate many function approximation problems. This article reviews the main theoretical GP developments in the field, considering new algorithms that respect signal and noise characteristics, extract knowledge via auto... View full abstract»

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  • Ground-Based Image Analysis: A Tutorial on Machine-Learning Techniques and Applications

    Publication Year: 2016, Page(s):79 - 93
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2953 KB)

    Ground-based whole-sky cameras have opened up new opportunities for monitoring the earth's atmosphere. These cameras are an important complement to satellite images by providing geoscientists with cheaper, faster, and more localized data. The images captured by whole-sky imagers (WSI) can have high spatial and temporal resolution, which is an important prerequisite for applications such as solar e... View full abstract»

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  • The Geoscience Spaceborne Imaging Spectroscopy Technical Committee's Calibration and Validation Workshop [Technical Committees]

    Publication Year: 2016, Page(s):94 - 97
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (879 KB)

    Calibration is the process of quantitatively defining a system's responses to known, controlled signal inputs, and validation is the process of assessing, by independent means, the quality of the data products derived from those system outputs [1]. Similar to other Earthobservation (EO) sensors, the calibration and validation of spaceborne imaging spectroscopy sensors is a fundamental underpinning... View full abstract»

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  • Reflectance-Based, Imaging Spectrometer Error Budget Training Course

    Publication Year: 2016, Page(s): 97
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  • The IEEE Wuhan Section Chapter Focuses on Remote Sensing Techniques [Chapters]

    Publication Year: 2016, Page(s):98 - 100
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  • The Joint Singapore Chapter Helps R&D Grow in Singapore [Chapters]

    Publication Year: 2016, Page(s):101 - 103
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  • GRSS Chapters and Contact Information [Chapters]

    Publication Year: 2016, Page(s):104 - 105
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  • A Distinguished Lecturer Can Give a Talk at Your Chapter Meeting [Distinguished Lecturer Program]

    Publication Year: 2016, Page(s): 106
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  • The Gender Underrepresentation Program Aims to Even Things Out [Women in GRS]

    Publication Year: 2016, Page(s):107 - 108
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  • GRSS Members Elevated to Senior Member in February 2016 [GRSS Member Highlights]

    Publication Year: 2016, Page(s): 109
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  • IEEE Geoscience and Remote Sensing Society's 2015 Best Reviewers [GRSS Member Highlights]

    Publication Year: 2016, Page(s):109 - 110
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  • [Calendar]

    Publication Year: 2016, Page(s): 111
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Aims & Scope

The IEEE Geoscience and Remote Sensing Magazine informs readers of activities in the IEEE GRS Society, its technical committees and chapters.

Full Aims & Scope

Meet Our Editors

Editor
Lorenzo Bruzzone
University of Trento
Department of Information Engineering and Computer Science