Multilevel Cloud Detection in Remote Sensing Images Based on Deep Learning
Multilevel Cloud Detection in Remote Sensing Images Based on Deep Learning
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DOI：
10.1109/jstars.2017.2686488
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发表时间：
2017-08-01
影响因子：
5.5
通讯作者：
Zhao, Danpei
中科院分区：
文献类型：
--
作者：
Xie, Fengying;Shi, Mengyun;Zhao, Danpei
云检测是遥感图像处理的重要任务之一。本文提出了一种基于深度学习的遥感图像多级云检测方法。首先，改进了简单线性迭代聚类（SLIC）方法，将图像分割成高质量的超像素。然后，设计具有两个分支的深度卷积神经网络（CNN），从每个超像素中提取多尺度特征，并将超像素预测为厚云、薄云和非云三类之一。最后，对图像中所有超像素的预测产生云检测结果。在所提出的云检测框架中，改进的SLIC方法可以通过优化初始聚类中心、设计动态距离度量和扩展搜索空间来获得准确的云边界。此外，与传统的云检测方法无法实现云的多级检测不同，所设计的深度CNN模型不仅可以检测云，还可以区分薄云和厚云。实验结果表明，与对比方法相比，该方法能够以更高的精度和鲁棒性检测云。
Cloud detection is one of the important tasks for remote sensing image processing. In this paper, a novel multilevel cloud detection method based on deep learning is proposed for remote sensing images. First, the simple linear iterative clustering (SLIC) method is improved to segment the image into good quality superpixels. Then, a deep convolutional neural network (CNN) with two branches is designed to extract the multiscale features from each superpixel and predict the superpixel as one of three classes including thick cloud, thin cloud, and noncloud. Finally, the predictions of all the superpixels in the image yield the cloud detection result. In the proposed cloud detection framework, the improved SLIC method can obtain accurate cloud boundaries by optimizing initial cluster centers, designing dynamic distance measure, and expanding search space. Moreover, different from traditional cloud detection methods that cannot achieve multilevel detection of cloud, the designed deep CNN model can not only detect cloud but also distinguish thin cloud from thick cloud. Experimental results indicate that the proposed method can detect cloud with higher accuracy and robustness than compared methods.