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Erschienen in:
Application of NSGA-II Algorithm to Energy and Spectral Efficiency Trade-off in Massive MIMO Systems with Antenna Selection Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Deep Learning and Machine Learning for Object Detection in Remote Sensing Images

verfasst von : Guowei Yang, Qiang Luo, Yinding Yang, Yin Zhuang

Erschienen in: Signal and Information Processing, Networking and Computers

Verlag: Springer Singapore

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Abstract

Object detection is one of the most effective ways to analyze the remote sensing (RS) images. In this paper, we focus on the prevalent object detection framework based on deep learning technology for RS images which contains three different stages, namely the region proposals generation, feature extraction, and classification. The review provides a clear picture of the challenges and possible development trends in this field. Typical methods under this framework are extensively reviewed and analyzed. Comparisons among traditional methods with deep learning methods are presented, in which supervised and unsupervised methods for RS scene target detection are deeply discussed.

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Vorheriges Kapitel An Innovative Indoor Location Algorithm Based on Supervised Learning and WIFI Fingerprint Classification
Nächstes Kapitel Design and Implementation of Automatic Interpretation System for Remote Sensing Image
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Metadaten
Titel
Deep Learning and Machine Learning for Object Detection in Remote Sensing Images
verfasst von
Guowei Yang
Qiang Luo
Yinding Yang
Yin Zhuang
Copyright-Jahr
2018
Verlag
Springer Singapore
Buch
Signal and Information Processing, Networking and Computers

Print ISBN: 978-981-10-7520-9

Electronic ISBN: 978-981-10-7521-6

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-981-10-7521-6_30

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