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首页> 外文期刊>International journal of image and data fusion >Classification of SAR and PolSAR images using deep learning: a review
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Classification of SAR and PolSAR images using deep learning: a review

机译：利用深度学习的SAR和POLSAR图像分类：综述

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摘要

Advancement in remote sensing technology and microwave sensors explores the applications of remote sensing in different fields. Microwave remote sensing encompasses its benefits of providing cloud-free, all-weather images and images of day and night. Synthetic Aperture Radar (SAR) images own this capability which promoted the use of SAR and PolSAR images in land use/land cover classification and various other applications for different purposes. A review of different polarimetric decomposition techniques for classification of different regions is introduced in the paper. The general objective of the paper is to help researchers in identifying a deep learning technique appropriate for SAR or PolSAR image classification. The architecture of deep networks which ingest new ideas in the given area of research are also analysed in this paper. Benchmark datasets used in microwave remote sensing have been discussed and classification results of those data are analysed. Discussion on experimental results on one of the benchmark datasets is also provided in the paper. The paper discusses challenges, scope and opportunities in research of SAR/PolSAR images which will be helpful to researchers diving into this area.
机译：遥感技术和微波传感器的进步探讨了遥感在不同领域的应用。微波遥感包括提供无云，全天候图像和白天和夜间图像的好处。合成孔径雷达（SAR）图像拥有这种能力，该功能促进了在土地使用/陆地覆盖分类和各种其他应用中使用SAR和POLSAR图像以进行不同目的。介绍了对不同地区分类的不同偏振分解技术的综述。本文的一般目标是帮助研究人员识别适合SAR或POLSAR图像分类的深度学习技术。本文还分析了在研究中摄取新思路的深网络建筑。已经讨论了微波遥感中使用的基准数据集，并分析了这些数据的分类结果。本文还提供了关于其中一个基准数据集的实验结果的讨论。本文讨论了SAR / POLSAR图像研究的挑战，范围和机遇，这将有助于研究人员进入该地区的研究人员。

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