Remote sensing has evolved into a multidisciplinary field involving many different areas such as sensor technology, computing, and advanced applications. Information extraction now plays a fundamental role in the exploitation of the massive amount of data collected by earth observation instruments. In this Point of View, the authors analyze the evolution of this field, identifying three main phases in its development. The first period, which was marked by advances in digital signal processing, led to a significant development of statistical processing methods. The second phase was based on advances in physical models and brought an era of quantitative remote sensing which lasted until the first decade of this century. In the third and current period, information extraction techniques are gradually adopting advanced artificial intelligence models in an effort to cope with the tremendous increase in data volume. This article describes some of these recent advances and addresses challenges caused by the 4Vs (volume, velocity, variety, and veracity) of big data. Finally, the authors offer insight into future directions in this multidisciplinary field.

中文翻译：

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扫描问题

遥感已经发展成为一个涉及多个领域的跨学科领域，例如传感器技术，计算和高级应用。现在，信息提取在利用地球观测仪器收集的大量数据中起着根本性的作用。在这种观点下，作者分析了该领域的发展，确定了该领域的三个主要发展阶段。第一阶段以数字信号处理的进步为标志，导致统计处理方法的显着发展。第二阶段基于物理模型的进步，带来了一个定量遥感的时代，这个时代一直持续到本世纪的前十年。在第三期和当前期间，信息提取技术正逐渐采用高级人工智能模型，以应对数据量的巨大增长。本文介绍了其中的一些最新进展，并解决了大数据的4V（容量，速度，多样性和准确性）带来的挑战。最后，作者对这一多学科领域的未来方向提供了见识。