An Unsupervised Snow Segmentation Approach Based on Dual-polarized Scattering Mechanism and Deep Neural Network

Distribution of snow and its melting is a critical factor affecting local weather, avalanche and flood forecasting, livelihood of people residing, and hydropower production. Most of the existing dry and wet snow identification methods were based on expensive quad-pol SAR with finite generalizability...

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Published in:	IEEE transactions on geoscience and remote sensing Vol. 61; p. 1
Main Authors:	Liu, Chang, Li, Zhen, Wu, Zhipeng, Huang, Lei, Zhang, Ping, Li, Gang
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.01.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Accuracy Algorithms Artificial neural networks C band Clustering Dry and wet snow Dual polarization radar Feature extraction feature-weighted fuzzy C-means Flood forecasting Hydroelectric power Identification methods Melting Methods Neural networks polarimetric decomposition Polarimetry Radar polarimetry River discharge SAR (radar) Scattering Snow Snow avalanches Snowmelt sparse autoencoder Speckle Support vector machines Synthetic aperture radar Training Weather forecasting
ISSN:	0196-2892, 1558-0644
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Abstract	Distribution of snow and its melting is a critical factor affecting local weather, avalanche and flood forecasting, livelihood of people residing, and hydropower production. Most of the existing dry and wet snow identification methods were based on expensive quad-pol SAR with finite generalizability, while dual-pol SAR with larger coverage, longer time series and open availability has more advantages. In this study, an unsupervised algorithm for dry and wet snow discrimination, NSAE-WFCM, is proposed based on a variety of polarimetric features derived from H-α decomposition in dual-pol mode using C-band Sentinel-1 SAR data. NSAE-WFCM constructs a deep training network using the pixel neighborhood-based sparse autoencoder (NSAE) to optimize polarimetric parameters, and inputs reconstructed features with different weights into feature-weighted fuzzy C-means clustering (WFCM) to distinguish dry and wet snow for each underlying surface. Ground observation was carried out during the snow melting period of March 2021 in Altay, China, to validate dual-pol NSAE-WFCM method with an overall accuracy and kappa coefficient of 88.8% and 0.68, respectively. The results show that NSAE-WFCM's accuracy is similar to that of the quad-pol SAR-based dry and wet snow result (90.0%), and significantly better than that of previously published approaches extended to dual-pol SAR, such as SVM (76.7%), H-α-Wishart (65.5%), SPAN-based threshold method (51.7%), and wet snow-based method (43.1%). Therefore, the NSAE-WFCM algorithm improves the ability to classify wet and dry snow based on dual-pol polarimetric features, overcomes the high dependence of existing methods on quad-pol SAR data, and reduces manual interpretation by using unsupervised clustering.
AbstractList	Distribution of snow and its melting is a critical factor affecting local weather, avalanche and flood forecasting, livelihood of people residing, and hydropower production. Most of the existing dry and wet snow identification methods were based on expensive quad-pol SAR with finite generalizability, while dual-pol SAR with larger coverage, longer time series and open availability has more advantages. In this study, an unsupervised algorithm for dry and wet snow discrimination, NSAE-WFCM, is proposed based on a variety of polarimetric features derived from H-α decomposition in dual-pol mode using C-band Sentinel-1 SAR data. NSAE-WFCM constructs a deep training network using the pixel neighborhood-based sparse autoencoder (NSAE) to optimize polarimetric parameters, and inputs reconstructed features with different weights into feature-weighted fuzzy C-means clustering (WFCM) to distinguish dry and wet snow for each underlying surface. Ground observation was carried out during the snow melting period of March 2021 in Altay, China, to validate dual-pol NSAE-WFCM method with an overall accuracy and kappa coefficient of 88.8% and 0.68, respectively. The results show that NSAE-WFCM's accuracy is similar to that of the quad-pol SAR-based dry and wet snow result (90.0%), and significantly better than that of previously published approaches extended to dual-pol SAR, such as SVM (76.7%), H-α-Wishart (65.5%), SPAN-based threshold method (51.7%), and wet snow-based method (43.1%). Therefore, the NSAE-WFCM algorithm improves the ability to classify wet and dry snow based on dual-pol polarimetric features, overcomes the high dependence of existing methods on quad-pol SAR data, and reduces manual interpretation by using unsupervised clustering. Distribution of snow and its melting is a critical factor affecting local weather, avalanche and flood forecasting, livelihood of people residing, and hydropower production. Most of the existing dry and wet snow identification methods were based on expensive quad-pol synthetic aperture radar (SAR) with finite generalizability, while dual-pol SAR with larger coverage, longer time series, and open availability has more advantages. In this study, an unsupervised algorithm for dry and wet snow discrimination, neighborhood-based sparse autoencoder (NSAE)-weighted fuzzy C-means clustering (WFCM), is proposed based on a variety of polarimetric features derived from the [Formula Omitted]–[Formula Omitted] decomposition in the dual-pol mode using the C-band Sentinel-1 SAR data. NSAE-WFCM constructs a deep training network using the pixel NSAE to optimize polarimetric parameters and inputs reconstructed features with different weights into feature-WFCM to distinguish dry and wet snow for each underlying surface. Ground observation was carried out during the snow melting period of March 2021 in Altay, China, to validate the dual-pol NSAE-WFCM method with an overall accuracy and a Kappa coefficient of 88.8% and 0.68, respectively. The results show that NSAE-WFCM’s accuracy is similar to that of the quad-pol SAR-based dry and wet snow result (90.0%) and significantly better than that of previously published approaches extended to dual-pol SAR, such as support vector machine (SVM) (76.7%), H–[Formula Omitted]-Wishart (65.5%), total power-based method (51.7%), and wet snow-based method (43.1%). Therefore, the NSAE-WFCM algorithm improves the ability to classify wet and dry snow based on dual-pol polarimetric features, overcomes the high dependence of existing methods on quad-pol SAR data, and reduces manual interpretation by using unsupervised clustering.
Author	Wu, Zhipeng Li, Zhen Huang, Lei Li, Gang Zhang, Ping Liu, Chang
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SubjectTerms	Accuracy Algorithms Artificial neural networks C band Clustering Dry and wet snow Dual polarization radar Feature extraction feature-weighted fuzzy C-means Flood forecasting Hydroelectric power Identification methods Melting Methods Neural networks polarimetric decomposition Polarimetry Radar polarimetry River discharge SAR (radar) Scattering Snow Snow avalanches Snowmelt sparse autoencoder Speckle Support vector machines Synthetic aperture radar Training Weather forecasting
Title	An Unsupervised Snow Segmentation Approach Based on Dual-polarized Scattering Mechanism and Deep Neural Network
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