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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Contextual reconstruction of cloud-contaminated multitemporal multispectral images
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Contextual reconstruction of cloud-contaminated multitemporal multispectral images

机译：云污染的多时间多光谱图像的上下文重建

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摘要

The frequent presence of clouds in passive remotely sensed imagery severely limits its regular exploitation in various application fields. Thus, the removal of cloud cover from this imagery represents an important preprocessing task consisting in the reconstruction of cloud-contaminated data. The intent of this study is to propose two novel general methods for the reconstruction of areas obscured by clouds in a sequence of multitemporal multispectral images. Given a cloud-contaminated image of the sequence, each area of missing measurements is reconstructed through an unsupervised contextual prediction process that reproduces the local spectro-temporal relationships between the considered image and an opportunely selected subset of the remaining temporal images. In the first method, the contextual prediction process is implemented by means of an ensemble of linear predictors, each trained over a local multitemporal region that is spectrally homogeneous in each temporal image of the selected subset. In order to obtain such regions, each temporal image is locally classified by an unsupervised classifier based on the expectation-maximization (EM) algorithm. In the second method, the local spectro-temporal relationships are reproduced by a single nonlinear predictor based on the support vector machines (SVM) approach. To illustrate the performance of the two proposed methods, an experimental analysis on a sequence of three temporal images acquired by the Landsat-7 Enhanced Thematic Mapper Plus sensor over a total period of four months is reported and discussed. It includes a detailed simulation study that aims at assessing with different reconstruction quality criteria the accuracy of the methods in different qualitative and quantitative cloud contamination conditions. Compared with two techniques based on compositing algorithms for cloud removal, the proposed methods show a clear superiority, which makes them a promising and useful tool in solving the considered problem, whose great complexity is commensurate with its practical importance.
机译：被动遥感影像中云的频繁出现严重限制了其在各种应用领域中的常规利用。因此，从该图像中去除云层代表了一项重要的预处理任务，即重建受云污染的数据。这项研究的目的是提出两种新颖的通用方法，用于重建一系列多时相多光谱图像中被云遮挡的区域。给定序列的云污染图像，可通过无监督的上下文预测过程来重建缺失测量的每个区域，该过程会重现考虑的图像与其余时间图像的适当选择子集之间的局部光谱时间关系。在第一种方法中，上下文预测过程是通过一组线性预测器来实现的，每个线性预测器都在局部多时间区域内训练，该区域在所选子集的每个时间图像中在频谱上是同质的。为了获得这样的区域，通过基于期望最大化（EM）算法的无监督分类器对每个时间图像进行局部分类。在第二种方法中，通过基于支持向量机（SVM）方法的单个非线性预测变量来再现局部光谱-时间关系。为了说明这两种建议方法的性能，报告并讨论了对Landsat-7增强型主题映射器Plus传感器在四个月的总时间内获取的三个时间图像序列的实验分析。它包括一个详细的模拟研究，旨在用不同的重建质量标准评估在不同的定性和定量云污染条件下方法的准确性。与两种基于合成算法的云去除技术相比，该方法具有明显的优越性，使其成为解决所考虑问题的有希望和有用的工具，其复杂性与其实用重要性相称。

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