6 research outputs found
Deep learning in remote sensing: a review
Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources
Central to the looming paradigm shift toward data-intensive science, machine-learning techniques are becoming increasingly important. In particular, deep learning has proven to be both a major breakthrough and an extremely powerful tool in many fields. Shall we embrace deep learning as the key to everything? Or should we resist a black-box solution? These are controversial issues within the remote-sensing community. In this article, we analyze the challenges of using deep learning for remote-sensing data analysis, review recent advances, and provide resources we hope will make deep learning in remote sensing seem ridiculously simple. More importantly, we encourage remote-sensing scientists to bring their expertise into deep learning and use it as an implicit general model to tackle unprecedented, large-scale, influential challenges, such as climate change and urbanization
Обработка радиолокационных изображений: монография
Книга посвящена решению теоретических и практических проблем обнаружения, измерения параметров и классификации пространственно-распределённых целей (ПРЦ) по их радиолокационным изображениям (РЛИ), формируемым в многопозиционной системе наблюдения, реализованной группой космических аппаратов. В книге подробно рассмотрены методы синтеза и анализа алгоритмов классификации ПРЦ, алгоритмы оценки параметров РЛИ, алгоритмы классификации с использованием нейронных сетей, частично-когерентных РЛС, алгоритмы формирования РЛИ движущихся объектов, методы фильтрации спекл-шума, методы анализа помехоустойчивости, методы геокоррекции формируемых РЛИ.
Книга представляет интерес для специалистов, студентов и аспирантов, работающих в области разработки современных радиотехнических систем военного и гражданского назначения