Cloud detection for FY-3D MERSI Ⅱ images combine radiative transfer simulation and shallow neural network
- Vol. 26, Issue 11, Pages: 2136-2146(2022)
Published： 07 November 2022
DOI： 10.11834/jrs.20210406
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摘要
本文结合辐射传输模型和机器学习提出了一种从FY-3D卫星MERSI Ⅱ传感器光学影像中识别云像元的方法CRMC（Combine Reflectance simulation and Machine learning for Cloud detection）。该方法通过设置变化的地物和大气内在光学特性IOPs（Inherent Optical Properties），达到考虑多种下垫面的二项反射特征和不同大气条件下气溶胶和云参数的目的。CRMC方法主要包含3个步骤：（1）通过聚类分析从MODIS二项反射参数产品中分离出11种典型下垫面地表反射参数；（2）将随机设置的气溶胶和云参数以及地表反射率参数（即IOPs）输入SBDART辐射传输模型，得到模拟的反射率值数据集，并以此训练浅层神经网络模型；（3）利用浅层神经网络模型逐像元预测云概率，并根据实际需要确定区分云像元和非云像元的云概率阈值。通过与CALIPSO垂直特性掩膜产品（VFM）逐像元对比验证发现，CRMC方法总正确率为79.6%，且在陆地和海面上分别为78.5%和81.2%。通过与MODIS云掩膜产品横向对比（MYD35）发现，当云阈值设定为0.2时，CRMC方法在陆地，主要是阔叶林、农田、城市和裸土等地表上的云识别效果较好，但在海面的云识别效果仍待进一步提高。
Abstract
Cloud pixel detection is a crucial pre-process in numerous remote sensing applications
such as the aerosol parameter retrieval
land use change
anomaly detection/classification
crop monitoring
and marine ecological survey. On the one hand
cloud pixel misidentification (mistaken as surface or aerosol) in optical images has substantial negative effects on the above-mentioned traditional applications
due to the significant influence of cloud layers on shortwave radiation. On the other hand
traditional threshold methods are largely constrained in applicability by the high spatiotemporal heterogeneity of aerosols and clouds as well as the diversification of satellite sensors and spectral channels. Therefore
a reliable cloud detection method with wide applicability is in demand
especially for studies over multiple surfaces (i.e.
land
ocean
and cryosphere). Thus
this study proposed an innovative method based on the radiative transfer simulation and machine learning
namely CRMC (Combine Reflectance simulation and Machine learning for Cloud detection)
to detect cloud pixels in optical images produced from the MERSI Ⅱ sensor onboard the FY-3D satellite. The biggest advantage of this method is its compatibility with different sensors
generating cloud pixel samples through the physical process simulation
and applying the machine learning technique to learn sample features
thereby excluding the influence of anthropogenic factors. Specifically
to address the mismatch between the MODIS cloud detection algorithm and MERSI-based aerosol inversion
the CRMC method sets different Inherent Optical Properties (IOPs) of surface and atmospheric objects
considering binomial reflection characteristics of underlying surfaces and various parameters of aerosols and clouds. In addition
the method outputs cloud probabilities in pixels and allows custom thresholds to control the strictness level of cloud pixel detection. The CRMC method mainly includes three steps: (1) Defining 11 typical underlying reflectance parameters from MODIS binomial reflection products using a cluster analysis approach; (2) Inputting the typical underlying reflectance and aerosol and cloud parameters with random inherent optical properties into the SBDART radiation transmission model to obtain a simulated reflectance dataset for training the shallow neural network; (3) Calculating the cloud probability of the target image with the trained shallow neural network and selecting a suitable threshold according to the actual need to complete the cloud detection. Compared with the CALIPSO Vertical Feature Mask (VFM)
results of the CRMC method show a maximum total accuracy of 79.6% (78.5% and 81.2% on land and sea
respectively). Under the condition of cloud probability threshold=0.2 (hit rates for cloud and cloud-free pixels are the same in these cases)
the CRMC outperforms MODIS cloud mask products (MYD35) over land
especially on broad-leaved forest
farmland
urban and bare soil. However
the accuracy of the CRMC over sea is lower than that of MYD35. In the view of the surface uniformity
the cloud detection over sea can enrich the brightness temperature information to optimize the corresponding performance. To sum up
while greatly improving the applicability to different optical sensors by not relying on special spectral range
the CRMC method can achieve a good cloud pixel identification effect for FY-3D MERSI Ⅱ images
with a similar hit rate compared with MODIS products. Also
the CRMC has certain anti-disturbance ability to haze.
关键词
云检测辐射传输模拟神经网络FY-3DMERSI II
Keywords
cloud detectionradiative transfer simulationneural networkFY-3DMERSI II
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Key Laboratory of Space Ocean Remote Sensing and Application, Ministry of Natural Resources of China
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Sarya Oceanographic Institution, Ocean University of China
College of Marine Technology, Faculty of Information Science and Engineering, Ocean University of China
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