用自适应神经网络提升多分辨率遥感图像融合的抗云干扰能力。
Robust Recursive Fusion of Multiresolution Multispectral Images with Location-Aware Neural Networks
- 引入位置感知神经网络建模图像动态，识别像素污染概率。
- 在Landsat 8与MODIS数据上，云覆盖下融合精度显著提升。
- 适合需要实时、鲁棒遥感图像处理的研究者和工程师。
多分辨率图像融合是实时卫星成像的关键问题，在洪水等自然现象的检测与监测中具有核心作用，旨在解决遥感仪器在时间与空间分辨率之间的权衡。尽管已有多种算法提出，但云等异常值会显著降低其性能。此外，现有方法普遍缺乏鲁棒性、递归处理机制与学习模型的结合。本文提出一种基于位置感知神经网络（NN）的鲁棒递归图像融合框架，通过建模像素与波段的污染概率来识别异常值。利用小规模数据集训练的神经网络可准确预测图像的随机时序演化，从而提升方法的精度与鲁棒性。采用贝叶斯变分推断框架实现递归高分辨率图像估计。在Landsat 8与MODIS数据上的实验表明，该方法在存在云覆盖时表现显著更优，且在无云条件下不损失性能。
原文摘要 · Abstract (English)
Multiresolution image fusion is a key problem for real-time satellite imaging and plays a central role in detecting and monitoring natural phenomena such as floods. It aims to solve the trade-off between temporal and spatial resolution in remote sensing instruments. Although several algorithms have been proposed for this problem, the presence of outliers such as clouds downgrades their performance. Moreover, strategies that integrate robustness, recursive operation and learned models are missing. In this paper, a robust recursive image fusion framework leveraging location-aware neural networks (NN) to model the image dynamics is proposed. Outliers are modeled by representing the probability of contamination of a given pixel and band. A NN model trained on a small dataset provides accurate predictions of the stochastic image time evolution, which improves both the accuracy and robustness of the method. A recursive solution is proposed to estimate the high-resolution images using a Bayesian variational inference framework. Experiments fusing images from the Landsat 8 and MODIS instruments show that the proposed approach is significantly more robust against cloud cover, without losing performance when no clouds are present.
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