Extreme Self-Paced Learning Machine for On-Orbit SAR Images Change Detection
نویسندگان
چکیده
منابع مشابه
Change detection from synthetic aperture radar images based on neighborhood-based ratio and extreme learning machine
Change detection is of high practical value to hazard assessment, crop growth monitoring, and urban sprawl detection. A synthetic aperture radar (SAR) image is the ideal information source for performing change detection since it is independent of atmospheric and sunlight conditions. Existing SAR image change detection methods usually generate a difference image (DI) first and use clustering me...
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Change detection in remote sensing images becomes more and more important for the last few decades, among them change detection in Synthetic Aperture Radar (SAR) images are having some more difficulties than optical ones due to the fact that SAR images suffer from the presence of the speckle noise. In this paper the Systematic survey of the common processing steps and core decision rules for ch...
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........................................................................................ I SAMMANFATTNING .................................................................... IV ACKNOWLEDGEMENTS ........................................................... VII TABLE OF CONTENTS ................................................................. IX LIST OF FIGURES ......................................
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................................................................................................ i Acknowledgements ............................................................................. iii
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In this paper, we present a novel unsupervised change detection approach in temporal sets of synthetic aperture radar (SAR) images using Markovian fusion. This method is carried out within a Markovian framework which combines two different change detection algorithms to achieve noise removing and spatial information preserving at the same time. This approach is composed of two steps: 1) two cha...
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2019
ISSN: 2169-3536
DOI: 10.1109/access.2019.2934983