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Generalization ability of cloud detection network for satellite imagery based on DeepLabv3+

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

    Beijing Engineering Research Center for Globe Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

  • Email：201921051193@mail.bnu.edu.cn
  • Introduction：彭龙康，1997年生，男，硕士研究生，研究方向为事遥感影像云检测。E-mail: 201921051193@mail.bnu.edu.cn
PENG Longkang12,  
  • Affiliation：

    State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

    Beijing Engineering Research Center for Globe Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

LIU Licong12,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

    Beijing Engineering Research Center for Globe Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

  • Email：chenxuehong@bnu.edu.cn
  • Introduction：陈学泓，1985年生，男，副教授，研究方向为遥感图像融合、土地覆盖监测。E-mail: chenxuehong@bnu.edu.cn
CHEN Xuehong12*,  
  • Affiliation：

    State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

    Beijing Engineering Research Center for Globe Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

CHEN Jin12,  
  • Affiliation：

    State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

    Beijing Engineering Research Center for Globe Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

CAO Xin12,  
  • Affiliation：

    State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

    Beijing Engineering Research Center for Globe Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

QIU Yuean12

résumé

Deep learning algorithms have been developed and applied in detecting clouds for satellite imagery in recent years. However, deep neural network models consist of thousands or millions of parameters, thus usually requiring large amounts of training data. Therefore, understanding the generalization ability of deep learning techniques is vital in their application to cloud detection of different types of satellite imagery. Taking DeepLabv3+, a typical deep semantic segmentation algorithm, as an example, this study explored the generalization ability of the algorithm on the cloud detection of satellite imagery with different landscapes, spatial resolutions, and spectral band combinations based on the cloud labeled dataset “L8 Biome.” The “L8 Biome” dataset consists of 96 typical Landsat 8 OLI images and the corresponding manual cloud mask, which has been widely used for evaluating the performance of cloud and cloud shadow detection methods. First, the cloud labeled dataset “L8 Biome” was used to generate different training and test samples with different landscapes, spatial resolutions, and band combinations. Then, the performance of DeepLabv3+ was evaluated based on different training and test sets and compared with that of the typical Function of Mask (Fmask) algorithm. Results show the following: (1) the DeepLabv3+ trained by a fully mixed training set (consisting of imagery captured over all landscapes) has higher overall cloud accuracy (92.81%) and stability (standard deviation 12.08%) than that trained by the training set of imagery captured over a single landscape and performed better than the Fmask algorithm with an overall cloud accuracy of 88.75% and stability of 17.34%, indicating that the training set of the deep learning algorithm should include images captured over various landscape types; (2) the DeepLabv3+ trained by “mixed-1” training sets, which were built by removing the images captured over a single landscape type (except of snow/ice) from the fully mixed training set, achieved comparable accuracies with that trained by a fully mixed training set, indicating that the available training set is diverse enough and has a good generalization ability on imagery captured over different landscapes; (3) the DeepLabv3+ trained by a fully mixed training set with a 30-m resolution achieved similar cloud detection accuracies on the satellite images with different spatial resolutions (i.e., 30, 60, 120, and 240 m), indicating that the trained DeepLabv3+ could be directly applied on satellite imagery with different spatial resolution, whereas the Fmask algorithm performed poorly on the images with coarse resolutions; (4) DeepLabv3+ could explore the effective information from different spectral bands for cloud detection, and more spectral band input can generally improve the overall cloud accuracy and stability of DeepLabv3+. Among all input bands, shortwave infrared bands are greatly helpful for distinguishing snow/ice from clouds, whereas the thermal infrared band marginally improves the cloud detection accuracy for DeepLabv3+. Results indicate that the DeepLabv3+ cloud detection network trained by the “L8 Biome” dataset can be applied to various types of satellite imagery and outperforms the Fmask algorithm.

mots-clés

deep learning;cloud detection;DeepLabv3+;generalization ability;landscape;spectral band combination;spatial resolution

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