Automatic cloud detection algorithm based on deep belief network-Otsu hybrid model

doi:10.11772/j.issn.1001-9081.2018041350

Journal of Computer Applications ›› 2018, Vol. 38 ›› Issue (11): 3175-3179.DOI: 10.11772/j.issn.1001-9081.2018041350

Previous Articles Next Articles

Automatic cloud detection algorithm based on deep belief network-Otsu hybrid model

QIU Meng, YIN Haoyu, CHEN Qiang, LIU Yingjian

Department of Computer Science and Technology, Ocean University of China, Qingdao Shandong 266100, China

Received:2018-04-30 Revised:2018-06-12 Online:2018-11-10 Published:2018-11-10
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61572448, 61673357), the Natural Science Foundation of Shandong Province (ZR2014JL043), the Key R&D Program of Shandong Province (2018GSF120015).

基于深度置信网络Otsu混合模型的自动云检测算法

邱梦, 尹浩宇, 陈强, 刘颖健

中国海洋大学计算机科学与技术系, 山东青岛 266100

通讯作者: 刘颖健
作者简介:邱梦(1992-),女,山东高密人,硕士研究生,主要研究方向:大数据、机器学习;尹浩宇(1994-),男,重庆人,硕士研究生,CCF会员,主要研究方向:机器学习、无线传感器网络、计算机视觉;陈强(1987-),男,山东泰安人,硕士研究生,主要研究方向:数据挖掘、机器学习;刘颖健(1974-),女,山东滨州人,副教授,博士,CCF会员,主要研究方向:海洋大数据、机器学习、卫星遥感、海洋传感器网络。
基金资助:
国家自然科学基金资助项目（61572448，61673357）；山东省自然科学基金资助项目（ZR2014JL043）；山东省重点研发计划项目（2018GSF120015）。

Abstract

Abstract: More than half of the earth's surface is covered by cloud. Current cloud detection methods from satellite remote sensing imageries are mainly manual or semi-automatic, depending upon manual intervention with low efficiency. Such methods can hardly be utilized in real-time or quasi real-time applications. To improve the availability of satellite remote sensing data, an automatic cloud detection method based on Deep Belief Network (DBN) and Otsu's method was proposed, named DOHM (DBN-Otsu Hybrid Model). The main contribution of DOHM is to replace the empirical fixed thresholds with adaptive ones, therefore achieve full-automatic cloud detection and increase the accuracy to greater than 95%. In addition, a 9-dimensional feature vector is adopted in network training. Diversity of the input feature vector helps to capture the characteristics of cloud more effectively.

Key words: deep learning, cloud detection, Deep Belief Network (DBN), Otsu's method, Advanced Very High Resolution Radiometer (AVHRR)

摘要： 地球表面一半以上被云覆盖，卫星遥感图像中的云检测主要是人工检测识别或者半自动化方法，依赖人工干预，效率不高，难以满足实时或准实时应用的需要。为了提高卫星遥感数据的可用性，基于深度置信网络（DBN）和最大类间方差法，提出一种自动云检测算法——DOHM。该算法采用自适应阈值代替人工标定阈值，实现云检测的全自动化，将云检测的正确率提高到95%以上；DOHM算法选取了维度为9的特征向量作为检测网络的输入，输入特征向量的多样性，有利于网络更全面有效地捕捉到云的特点。

关键词: 深度学习, 云检测, 深度置信网络, 最大类间方差法, 高级甚高分辨率辐射计

CLC Number:

TP317.4

QIU Meng, YIN Haoyu, CHEN Qiang, LIU Yingjian. Automatic cloud detection algorithm based on deep belief network-Otsu hybrid model[J]. Journal of Computer Applications, 2018, 38(11): 3175-3179.

邱梦, 尹浩宇, 陈强, 刘颖健. 基于深度置信网络Otsu混合模型的自动云检测算法[J]. 计算机应用, 2018, 38(11): 3175-3179.

References

[1] CAZORLA A, HUSILLOS C, ANTON M. Multi-exposure adaptive threshold technique for cloud detection with sky imagers[J]. Solar Energy, 2015, 114(2):268-277.
[2] 高贤君,万幼川,郑顺义,等.航空摄影过程中云的实时自动检测[J].光谱学与光谱分析,2014,34(7):1909-1913.(GAO X J, WAN Y C, ZHENG S Y, et al. Real-time automatic cloud detection during the process of taking aerial photographs[J]. Spectroscopy and Spectral Analysis, 2014,34(7):1909-1913.)
[3] LIU S, ZHANG Z, HAN L. Ground-based cloud detection using automatic graph cut[J]. IEEE Geoscience & Remote Sensing Letters, 2017, 12(6):1342-1346.
[4] LI Q, LU W, YANG J. A hybrid thresholding algorithm for cloud detection on ground-based color images[J]. Journal of Atmospheric and Oceanic Technology, 2011, 28(10):1286-1296.
[5] LI P, DONG L, XIAO H, et al. A cloud image detection method based on SVM vector machine[J]. Neurocomputing, 2015, 169(6):34-42.
[6] YUAN Y, HU X. Bag-of-words and object-based classification for cloud extraction from satellite imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(8):4197-4205.
[7] XU L, WONG A, CLAUSI D. A novel Bayesian spatial-temporal random field model applied to cloud detection from remotely sensed imagery[J]. IEEE Transactions on Geoscience & Remote Sensing, 2017, 55(9):4913-4924.
[8] ZHANG Q, XIAO C.Cloud detection of RGB color aerial photographs by progressive refinement scheme[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(11):7264-7275.
[9] XIE F, SHI M, SHI Z. Multilevel cloud detection in remote sensing images based on deep learning[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(8): 3631-3640.
[10] 陈洋,范荣双,王竞雪.基于深度学习的资源三号卫星遥感影像云检测方法[J].光学学报,2018,38(1):354-359.(CHEN Y, FAN R S, WANG J X. Cloud detection of ZY-3 satellite remote sensing images based on deep learning[J].Acta Optica Sinica, 2018,38(1): 354-359.)
[11] SHI C, WANG Y, WANG C. Ground-based cloud detection using graph model built upon superpixels[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(5): 719-723.
[12] ZHONG B, CHEN W, WU S. A cloud detection method based on relationship between objects of cloud and cloud-shadow for chinese moderate to high resolution satellite imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2017, 10(11): 4868-4908.
[13] ISLAM T, RICO-RAMIREZ M A, SRIVASTAVA P K. CLOUDET: a cloud detection and estimation algorithm for passive microwave imagers and sounders aided by naive Bayes classifier and multilayer perceptron[J]. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2015, 8(9): 4296-4301.
[14] HINTON G E, NEAL R M. The "wake-sleep" algorithm for unsupervised neural networks[J]. Science, 1995, 268(5214): 1158.
[15] OTSU N. A threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems Man & Cybernetics, 1979, 9(1): 62-66.
[16] HAGOLLE O, HUC M. A multi-temporal method for cloud detection, applied to FORMOSAT-2, VENμS, LANDSAT and SENTINEL-2 images[J]. Remote Sensing of Environment, 2010, 114(8): 1747-1755.
[17] HINTON G E. Training products of experts by minimizing contrastive divergence[J]. Neural Computation, 2002, 14(8): 1771-1800.
[18] AN Z, SHI Z. Scene learning for cloud detection on remote-sensing images[J]. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2015, 8(8): 4206-4222.

Automatic cloud detection algorithm based on deep belief network-Otsu hybrid model

基于深度置信网络Otsu混合模型的自动云检测算法

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

[1]	Yunchuan HUANG, Yongquan JIANG, Juntao HUANG, Yan YANG. Molecular toxicity prediction based on meta graph isomorphism network [J]. Journal of Computer Applications, 2024, 44(9): 2964-2969.
[2]	Shunyong LI, Shiyi LI, Rui XU, Xingwang ZHAO. Incomplete multi-view clustering algorithm based on self-attention fusion [J]. Journal of Computer Applications, 2024, 44(9): 2696-2703.
[3]	Yexin PAN, Zhe YANG. Optimization model for small object detection based on multi-level feature bidirectional fusion [J]. Journal of Computer Applications, 2024, 44(9): 2871-2877.
[4]	Jing QIN, Zhiguang QIN, Fali LI, Yueheng PENG. Diagnosis of major depressive disorder based on probabilistic sparse self-attention neural network [J]. Journal of Computer Applications, 2024, 44(9): 2970-2974.
[5]	Xiyuan WANG, Zhancheng ZHANG, Shaokang XU, Baocheng ZHANG, Xiaoqing LUO, Fuyuan HU. Unsupervised cross-domain transfer network for 3D/2D registration in surgical navigation [J]. Journal of Computer Applications, 2024, 44(9): 2911-2918.
[6]	Yuhan LIU, Genlin JI, Hongping ZHANG. Video pedestrian anomaly detection method based on skeleton graph and mixed attention [J]. Journal of Computer Applications, 2024, 44(8): 2551-2557.
[7]	Yanjie GU, Yingjun ZHANG, Xiaoqian LIU, Wei ZHOU, Wei SUN. Traffic flow forecasting via spatial-temporal multi-graph fusion [J]. Journal of Computer Applications, 2024, 44(8): 2618-2625.
[8]	Qianhong SHI, Yan YANG, Yongquan JIANG, Xiaocao OUYANG, Wubo FAN, Qiang CHEN, Tao JIANG, Yuan LI. Multi-granularity abrupt change fitting network for air quality prediction [J]. Journal of Computer Applications, 2024, 44(8): 2643-2650.
[9]	Zheng WU, Zhiyou CHENG, Zhentian WANG, Chuanjian WANG, Sheng WANG, Hui XU. Deep learning-based classification of head movement amplitude during patient anaesthesia resuscitation [J]. Journal of Computer Applications, 2024, 44(7): 2258-2263.
[10]	Huanhuan LI, Tianqiang HUANG, Xuemei DING, Haifeng LUO, Liqing HUANG. Public traffic demand prediction based on multi-scale spatial-temporal graph convolutional network [J]. Journal of Computer Applications, 2024, 44(7): 2065-2072.
[11]	Zhi ZHANG, Xin LI, Naifu YE, Kaixi HU. DKP： defending against model stealing attacks based on dark knowledge protection [J]. Journal of Computer Applications, 2024, 44(7): 2080-2086.
[12]	Yiqun ZHAO, Zhiyu ZHANG, Xue DONG. Anisotropic travel time computation method based on dense residual connection physical information neural networks [J]. Journal of Computer Applications, 2024, 44(7): 2310-2318.
[13]	Song XU, Wenbo ZHANG, Yifan WANG. Lightweight video salient object detection network based on spatiotemporal information [J]. Journal of Computer Applications, 2024, 44(7): 2192-2199.
[14]	Xun SUN, Ruifeng FENG, Yanru CHEN. Monocular 3D object detection method integrating depth and instance segmentation [J]. Journal of Computer Applications, 2024, 44(7): 2208-2215.
[15]	Yajuan ZHAO, Fanjun MENG, Xingjian XU. Review of online education learner knowledge tracing [J]. Journal of Computer Applications, 2024, 44(6): 1683-1698.