Distinguishing Cloud and Snow in Satellite Images via Deep Convolutional Network
Distinguishing Cloud and Snow in Satellite Images via Deep Convolutional Network
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DOI：
10.1109/lgrs.2017.2735801
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发表时间：
2017-08
影响因子：
4.8
通讯作者：
Y. Zhan;Jian Wang;Jianping Shi;Guangliang Cheng;Lele Yao;Weidong Sun
中科院分区：
文献类型：
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作者：
Y. Zhan;Jian Wang;Jianping Shi;Guangliang Cheng;Lele Yao;Weidong Sun
云和雪检测具有重要的遥感应用，同时它们由于一致的颜色分布和相似的局部纹理模式而具有相似的低级特征。因此，对于传统方法来说，从卫星图像中在像素级别准确区分云和雪始终是一项具有挑战性的任务。为了解决这个缺点，在这封信中，我们提出了一个深度学习系统，用全卷积神经网络在像素级对云和雪进行分类。具体来说，引入了一个专门设计的完全卷积网络，以从多光谱卫星图像中学习云和雪检测的深度模式。在此基础上，引入多尺度预测策略，实现了低层空间信息和高层语义信息的融合。最后，一个新的和具有挑战性的云和雪数据集手动标记训练和进一步评估所提出的方法。大量的实验表明，所提出的深度模型在定量和定性性能上都大大优于最先进的方法。
Cloud and snow detection has significant remote sensing applications, while they share similar low-level features due to their consistent color distributions and similar local texture patterns. Thus, accurately distinguishing cloud from snow in pixel level from satellite images is always a challenging task with traditional approaches. To solve this shortcoming, in this letter, we proposed a deep learning system to classify cloud and snow with fully convolutional neural networks in pixel level. Specifically, a specially designed fully convolutional network was introduced to learn deep patterns for cloud and snow detection from the multispectrum satellite images. Then, a multiscale prediction strategy was introduced to integrate the low-level spatial information and high-level semantic information simultaneously. Finally, a new and challenging cloud and snow data set was labeled manually to train and further evaluate the proposed method. Extensive experiments demonstrate that the proposed deep model outperforms the state-of-the-art methods greatly both in quantitative and qualitative performances.