Polarization Remote Sensing Cloud Detection Using Multi-Dimensional Information Fusion Deep Learning Network

Shu Li; Xingyuan Ji; Xiaoxue Chu; Song Ye; Ziyang Zhang; Yongying Gan; Xinqiang Wang; Fangyuan Wang

doi:10.3788/AOS241653

Acta Optica Sinica, Volume. 45, Issue 12, 1228001(2025)

Polarization Remote Sensing Cloud Detection Using Multi-Dimensional Information Fusion Deep Learning Network

Shu Li^1,3, Xingyuan Ji^1,3, Xiaoxue Chu^2,3、*, Song Ye^1,3, Ziyang Zhang^1,3, Yongying Gan^1,3, Xinqiang Wang^1,3, and Fangyuan Wang^1,3

Author Affiliations

¹School of Optoelectronic Engineering, Guilin University of Electronic Technology, Shanghai Institute of Optics and Fine Mechanics, Guilin 541004, Guangxi , China

²School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, Guangxi , China

³Guangxi Key Laboratory of Optoelectronic Information Processing, Guilin 541004, Guangxi , China

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Figures & Tables(11)

Fig. 1. Schematic diagram of MIFCD-Net model framework

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Fig. 2. Schematic diagram of MIFCD-Net model structure. (a) Preprocessing module; (b) spectral-polarization-spatial global multi-angle information perception module; (c) spectral-polarization-spatial global multi-angle information fusion module; (d) channel weight generation module

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Fig. 3. Prediction results and comparison of ocean and ice and snow surfaces. (a) RGB true image; (b) MIFCD-Net prediction; (c) MODIS prediction; (d) POLDER prediction; (e) MIFCD-Net compared to MODIS; (f) POLDER compared to MODIS

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Fig. 4. Mountain surface prediction results and comparison. (a) RGB true image; (b) MIFCD-Net prediction; (c) MODIS prediction; (d) POLDER prediction; (e) MIFCD-Net compared to MODIS; (f) POLDER compared to MODIS

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Fig. 5. Field surface prediction results and comparison. (a) RGB true image; (b) MIFCD-Net prediction; (c) MODIS prediction; (d) POLDER prediction; (e) MIFCD-Net compared to MODIS; (f) POLDER compared to MODIS

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Fig. 6. Prediction of ablation test. (a) Comparison of ablation multi-angle adaptive attention module and MODIS prediction results; (b) comparison of ablation spectral-polarization attention module and MODIS prediction results; (c) comparison of spatial global attention module and MODIS prediction results

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Fig. 7. Comparison of model predictions. (a) MIFCD-Net; (b) ResNet; (c) comparison of MIFCD-Net prediction with MODIS; (d) ResNet predicion compared to MODIS

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Table 1. Cloud detection test area information table

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Table 1. Cloud detection test area information table

Time	Longitude	Latitude	Surface type	Region
2013-01-01	113°18′5.508″E‒128°50′23.640″E	60°58′19.992″S‒66°28′19.992″S	Oceans and ice surface	Waters near Antarctica
2012-05-04	1°9′23.732″E‒9°56′45.931″W	49°34′59.995″N‒55°4′59.995″N	Mountain range	Saint Saëns, France
2012-08-20	86°9′38.322″E‒94°24′56.880″E	36°34′59.995″N‒42°4′59.995″N	Field	Iowa, USA

Table 2. Statistical table of different surface prediction results
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Table 2. Statistical table of different surface prediction results
Type Consistency betweenprediction andMODIS /% Consistency betweenPOLDER andMODIS /%
Oceans and ice surface 95.53 95.16
Mountain range 81.49 69.96
Field 75.98 72.82

Table 3. Ablation experiment prediction analysis table

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Table 3. Ablation experiment prediction analysis table

Type	Comparison between pre ablation model prediction and MODIS /%	Comparison between post ablation model prediction and MODIS /%
Multi-angle adaptive attention module	95.53	93.91
Spectral-polarization attention module	95.53	94.36
Spatial global attention module	95.53	94.47

Table 4. Model evaluation result
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Table 4. Model evaluation result
Model Accuracy /% MAE RMSE $R^{2}$
MIFCD-Net 0.9553 0.2899 0.5474 0.5517
ResNet101 0.9534 0.3685 0.5649 0.5226

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Shu Li, Xingyuan Ji, Xiaoxue Chu, Song Ye, Ziyang Zhang, Yongying Gan, Xinqiang Wang, Fangyuan Wang. Polarization Remote Sensing Cloud Detection Using Multi-Dimensional Information Fusion Deep Learning Network[J]. Acta Optica Sinica, 2025, 45(12): 1228001

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