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Remote Sensing Image Fusion (Signal and Image Processing of Earth Observations) (en Inglés)
Luciano Alparone; Bruno Aiazzi; Stefano Baronti; Andrea Garzelli (Autor)
·
Crc Press
· Tapa Blanda
Remote Sensing Image Fusion (Signal and Image Processing of Earth Observations) (en Inglés) - Luciano Alparone; Bruno Aiazzi; Stefano Baronti; Andrea Garzelli
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Origen: Estados Unidos
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Reseña del libro "Remote Sensing Image Fusion (Signal and Image Processing of Earth Observations) (en Inglés)"
A synthesis of more than ten years of experience, Remote Sensing Image Fusion covers methods specifically designed for remote sensing imagery. The authors supply a comprehensive classification system and rigorous mathematical description of advanced and state-of-the-art methods for pansharpening of multispectral images, fusion of hyperspectral and panchromatic images, and fusion of data from heterogeneous sensors such as optical and synthetic aperture radar (SAR) images and integration of thermal and visible/near-infrared images. They also explore new trends of signal/image processing, such as compressive sensing and sparse signal representations.The book brings a new perspective to a multidisciplinary research field that is becoming increasingly articulate and comprehensive. It fosters signal/image processing methodologies toward the goal of information extraction, either by humans or by machines, from remotely sensed images. The authors explain how relatively simple processing methods tailored to the specific features of the images may be winning in terms of reliable performance over more complex algorithms based on mathematical theories and models unconstrained from the physical behaviors of the instruments.Ultimately, the book covers the births and developments of three generations of RS image fusion. Established textbooks are mainly concerned with the earliest generation of methods. This book focuses on second generation methods you can use now and new trends that may become third generation methods. Only the lessons learned with second generation methods will be capable of fostering the excellence among the myriad of methods that are proposed almost every day by the scientific literature.
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