Abstract

Owing to the tradeoff between scanning swath and pixel size, currently no satellite Earth observation sensors are able to collect images with high spatial and temporal resolution simultaneously. This limits the application of satellite images in many fields, including the characterization of crop yields or the detailed investigation of human-nature interactions. Spatio-temporal fusion (STF) is a widely used approach to solve the aforementioned problem. Traditional STF methods reconstruct fine-resolution images under the assumption that changes are able to be transferred directly from one sensor to another. However, this assumption may not hold in real scenarios, owing to the different capacity of available sensors to characterize changes. In this paper, we model such differences as a bias, and introduce a new sensor bias-driven STF model (called BiaSTF) to mitigate the differences between the spectral and spatial distortions presented in traditional methods. In addition, we propose a new learning method based on convolutional neural networks (CNNs) to efficiently obtain this bias. An experimental evaluation on two public datasets suggests that our newly developed method achieves excellent performance when compared to other available approaches.

中文翻译：

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基于卷积神经网络的传感器偏置驱动的时空融合模型

摘要

由于扫描条带和像素大小之间的权衡，目前没有卫星地球观测传感器能够同时收集具有高空间和时间分辨率的图像。这限制了卫星图像在许多领域的应用，包括作物产量的表征或人与自然相互作用的详细研究。时空融合（STF）是解决上述问题的一种广泛使用的方法。传统的STF方法在可以将变化直接从一个传感器传递到另一个传感器的假设下，重建高分辨率图像。但是，由于可用传感器表征变化的能力不同，因此该假设在实际情况下可能不成立。在本文中，我们对偏差进行建模，例如偏差，并引入了新的传感器偏置驱动的STF模型（称为BiaSTF），以减轻传统方法中出现的光谱和空间失真之间的差异。此外，我们提出了一种基于卷积神经网络（CNN）的新学习方法，可以有效地获得这种偏见。对两个公共数据集的实验评估表明，与其他可用方法相比，我们的新开发方法具有出色的性能。