CDnet: CNN-Based Cloud Detection for Remote Sensing Imagery
CDnet: CNN-Based Cloud Detection for Remote Sensing Imagery
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CDnet：基于 CNN 的遥感图像云检测
DOI：
10.1109/tgrs.2019.2904868
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发表时间：
2019
影响因子：
8.2
通讯作者：
Li Kun
中科院分区：
文献类型：
--
作者：
Yang Jingyu;Guo Jianhua;Yue Huanjing;Liu Zhiheng;Hu Haofeng;Li Kun
云检测是遥感图像预处理的重要任务之一。本文利用RSI的缩略图(即预览图像)，包含了原始多光谱或全色图像的信息，有效地提取了云掩模。由于分辨率和光谱信息的丢失，使用缩略图检测云掩模比利用原始RSI检测云掩模更具挑战性。为了解决这一问题，我们提出了一种云检测神经网络(CDNet)，该网络具有编解码结构、特征金字塔模块(FPM)和边界细化(BR)块。FPM在不损失分辨率和覆盖率的情况下提取多尺度上下文信息；BR块细化对象边界；编解码器结构逐渐恢复与输入图像相同大小的分割结果。在ZY-3卫星缩略图云量验证数据集和其他两个验证数据集(GF-1 WFV云和云影验证数据和Landsat-8云覆盖评估验证数据)上的实验结果表明，该方法达到了准确的检测精度，并优于现有的几种方法。
Cloud detection is one of the important tasks for remote sensing image (RSI) preprocessing. In this paper, we utilize the thumbnail (i.e., preview image) of RSI, which contains the information of original multispectral or panchromatic imagery, to extract cloud mask efficiently. Compared with detection cloud mask from original RSI, it is more challenging to detect cloud mask using thumbnails due to the loss of resolution and spectrum information. To tackle this problem, we propose a cloud detection neural network (CDnet) with an encoder–decoder structure, a feature pyramid module (FPM), and a boundary refinement (BR) block. The FPM extracts the multiscale contextual information without the loss of resolution and coverage; the BR block refines object boundaries; and the encoder–decoder structure gradually recovers segmentation results with the same size as input image. Experimental results on the ZY-3 satellite thumbnails cloud cover validation data set and two other validation data sets (GF-1 WFV Cloud and Cloud Shadow Cover Validation Data and Landsat-8 Cloud Cover Assessment Validation Data) demonstrate that the proposed method achieves accurate detection accuracy and outperforms several state-of-the-art methods.