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Remote Sensing Technology and Application  2022, Vol. 37 Issue (6): 1328-1338    DOI: 10.11873/j.issn.1004-0323.2022.6.1328
Fractal Dimension and Angular Second Moment-assisted Convolutional Neural Network for Cloud and Snow Recognition
Bo Zhu(),Fangjie Zhong,Junsuo Zhao
Science & Technology on Integrated Information System Laboratory,Institute of Software,Chinese Academy of Sciences,Beijing 100190,China
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For space-based earth observation imaging missions, the cloud cover contained in an remote sensing image often determines whether the data is available. However, the spectral characteristics of cloud and snow are similar, which makes it difficult to distinguish them. The purpose of the research on cloud and snow recognition is to improve the ability to judge the validity data. So an novel method is designed to solve it. Firstly, an improved lightweight convolutional neural network, which is built based on the proposed DCP (Double Convolution Parallel) structure, is used as the backbone to classify the quasi-cloud (cloud, snow and highly reflective ground objects) and other ground objects. Secondly, the textures and gray features of cloud and snow and ground objects are analyzed by a binary tree network formed by fractal dimension and angular second moment for fine recognition in further. The network weight layers are only six (four convolutional layers and two full connection layers). The proposed method is trained on the data sets containing cloud, snow and cloud-snow with different ground sample resolutions from Tianzhi-1 and SPOT4/5/6 and Pleiades. When compared on the accuracy with reference methods, such as random forest, SVM, traditional methods and binary tree methods, our method provide an increasing accuracy to 89.08%. The experimental results shows: (1) The comparative experiment between network structures shows that the DCP could effectively improve the model ability of feature information extraction and promote faster convergence; (2) Texture features analysis of remote sensing images makes the recognition process not completely dependent on convolutional neural network, so as to reduce the network depth and weight parameters; (3) The combination of traditional remote sensing analysis method and neural network is better than one of them alone, which can improve the accuracy of cloud and snow recognition. The proposed method is suitable for cloud and snow recognition on panchromatic, multispectral and hyperspectral remote sensing imagery.

Key words:  Optical remote sensing      Cloud and snow recognition      Fractal dimension      Angular second moment      Convolutional neural network      Binary tree     
Received:  05 November 2021      Published:  15 February 2023
ZTFLH:  TP79  
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Bo Zhu
Fangjie Zhong
Junsuo Zhao

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Bo Zhu,Fangjie Zhong,Junsuo Zhao. Fractal Dimension and Angular Second Moment-assisted Convolutional Neural Network for Cloud and Snow Recognition. Remote Sensing Technology and Application, 2022, 37(6): 1328-1338.

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Fig.1  Parts of samples of cloud, snow and underlying surfaces in the different datasets










天智-1全色2 560×2 048606
SPOT4全色/多光谱2 000×2 0483010/20
SPOT5全色/多光谱2 000×2 000202.5/10
SPOT6全色/多光谱2 500×2 500301.5/6
Pleiades多光谱3 500×2 500202
Table 1  Information of data sets
Fig.2  Fractal dimension and angular second moment-assisted convolutional neural network
Max Pooling 12,2/p:1/s:1
Max Pooling 22,2/p:1/s:1
FC 164,16,1650
FC 2502
Learning rate0.01
Activation FunctionReLU
Initialized weightHe
Parameters updatingSGD[36]
Dropout1 ratio0.5
Dropout2 ratio0.5
Table 2  Backbone parameters
Fig.3  Comparison of training and testing accuracy between the plain and improved convolutional neural network
Fig.4  Comparison between the plain and improved CNN on validation dataset
Fig.5  Fractal dimension values of cloud, snow and underlying surfaces
Fig.6  Comparison of fractal dimension between cloud, snow and underlying surfaces
Fig.7  ASMs of cloud, snow and underlying surfaces
Fig.8  Comparison of ASMs between cloud, snow and underlying surfaces
Fig.9  Recognition of some cloud, snow and underlying surfaces by binary tree network
Fig.10  Effect of the five methods on different data
Fig.11  Comparison of the five methods
Table 3  Statistics of recognition results
Fig.12  Recognition on cloud, snow and underlying surfaces
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