Artificial intelligence, in particular a supervised and unsupervised machine learning approach, has been becoming an interest in the field of measurement and instrumentation. Many problems of classification can be faced by a machine learning approach. We know machine learning is a broad area of artificial intelligence that comprises some other lines of research and activities such as deep learning. Synthetic aperture radar (SAR) measurements by means of its sensors are of great interest in environmental monitoring, in particular in land classification. This paper presents findings related to measurements and characterization through land classification of an environmentally sensitive area in Italy over two different time periods in order to assess changing parameters. A deep learning algorithm has been designed and implemented, and a comparison has been established with a spectral density approach.

中文翻译：

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用于环境分类的SAR传感器测量：基于机器学习的性能

人工智能，尤其是有监督和无监督的机器学习方法，已经成为测量和仪器领域的关注点。机器学习方法可能面临许多分类问题。我们知道机器学习是人工智能的广阔领域，它包括其他一些研究和活动领域，例如深度学习。借助其传感器的合成孔径雷达（SAR）测量在环境监测中尤其是在土地分类中引起了极大的兴趣。本文介绍了通过对意大利一个环境敏感地区的土地分类在两个不同时间段进行测量和表征的结果，以评估变化的参数。已经设计并实现了深度学习算法，