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Change Detection with Heterogeneous Remote Sensing Data: From Semi-Parametric Regression to Deep Learning

机译：用异构遥感数据进行变更检测：从半参数回归到深度学习

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摘要

Change detection represents a major family of remote sensing image analysis techniques and plays a fundamental role in a variety of applications to environmental monitoring and disaster risk management. However, most change detection methods operate under the assumption that the multitemporal input data have been collected with the same (or very similar) acquisition modality - a possibly critical restriction in several applications. In this paper, the problem and the opportunities of change detection from multitemporal data acquired through heterogeneous modalities are addressed. Methodologically, this is a highly challenging data fusion problem, especially within an unsupervised framework. Here, these challenges and the methodological approaches proposed in the literature' which range from earlier semi-parametric regression to current deep learning architectures, are reviewed. Then, recent fully unsupervised techniques, based on spectral clustering, traditional image regression, and deep image-to-image translation, are briefly described.
机译：变更检测代表了遥感图像分析技术的主要系列，在各种应用中起着基本作用，以对环境监测和灾害风险管理。然而，大多数变化检测方法在假设中，通过相同（或非常相似的）采集方式收集多模型输入数据 - 在几种应用中可能的严重限制。在本文中，解决了通过异构模式获取的多立体数据的变化检测的问题和机会。方法论上讲，这是一个高度挑战的数据融合问题，尤其是在无监督的框架内。这里，综述了这些挑战和文献中提出的方法论方法，从早期的半参数回归到当前深度学习架构中的范围。然后，简要描述基于光谱聚类，传统图像回归和深图像到图像转换的最近完全无监督的技术。

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