1. Introduction
Sea ice, as a vital component of the oceanic system, exerts a substantial influence on the ocean’s physical characteristics, including temperature, salinity, and density. This, in turn, has a profound impact on ocean circulation and the broader climate system [
1]. Furthermore, sea ice can have varying degrees of detrimental effects on multiple industries, including shipping, offshore oil and gas exploration, as well as marine fisheries [
2]. Therefore, it is of paramount importance to conduct timely and precise monitoring of sea ice in order to preempt and alleviate the losses resulting from sea-ice disasters. The classification of sea ice, a pivotal element in the realm of sea-ice research, forms the cornerstone of sea-ice monitoring efforts. Diverse information pertaining to sea ice, including data on sea-ice concentration, extent, and thickness, can be derived from the outcomes of sea-ice classification [
3,
4,
5].
Many current sea-ice studies focus on the freezing period, but research on sea ice during the melting season is equally significant. The melting season is a dynamic and crucial phase in the sea-ice system, marking the melting and degradation of sea ice. During the melting process, sea ice absorbs a substantial amount of heat, playing a role in regulating the temperature of the surrounding water and the atmosphere, helping to prevent rapid temperature increases. Additionally, sea-ice melting alters seawater salinity and density, affecting the surface ocean circulation. Melting ice also results in a reduction in sea-ice coverage, resulting in an impact on the reflection of solar radiation and heat absorption in the ocean. Therefore, the melting season has profound implications for the climate and marine ecosystems in regions with sea ice [
6]. During the melting period, the atmosphere contains a higher water vapor content, which can result in the formation of clouds and fog. Traditional visible light and infrared remote sensing data perform poorly in such conditions. Microwave remote sensing, with its all-weather and all-day advantages, has become a practical tool for monitoring sea ice [
7]. Microwave remote sensing data provide essential support for better understanding and addressing the impacts of climate change.
Starting from June 1978 with the launch of the first spaceborne Synthetic Aperture Radar (SAR) satellite, Seasat, by the United States, humanity achieved around-the-clock and all-weather monitoring of sea ice for the first time. With the subsequent launch of other SAR systems and their extensive application in sea-ice monitoring, many countries and regions have established operational sea-ice monitoring capabilities [
8].
SAR has demonstrated excellent performance in various fields and has garnered significant attention from many countries, becoming a highly competitive and rapidly evolving technological field. Starting from 1990, SAR systems have evolved from the initial L-band HH single-polarization system to multi-frequency systems (L, C, X) with four polarization modes (HH, HV, VV, VH). Multi-dimensional SAR systems have broken free from the constraints of single-polarization or dual-polarization SAR data. Compared to single- or dual-polarization SAR, polarimetric Synthetic Aperture Radar (PolSAR) provides more information about sea ice, including phase information and higher spatial resolution, enhancing the potential of SAR in environmental monitoring [
9,
10,
11].
SAR belongs to the category of imaging sensors, and in the context of sea-ice classification research based on SAR data, researchers can use SAR images for classification and identification. For instance, R. De Abreu [
12] discussed the differences in open water, new ice, and gray ice on different polarization images and performed classification. Kwon et al. [
13] proposed a total variation optimal segmentation method for sea-ice SAR images, reducing image processing time while improving segmentation accuracy. Johannessen et al. [
14], based on texture features in sea-ice SAR images, utilized neural network algorithms and Bayesian discrimination for sea-ice image type recognition. Wang et al. [
15] introduced a segmentation and classification method for sea-ice SAR images based on Markov random field theory and ice condition maps, which effectively suppressed the impact of speckle noise and enhanced image segmentation and classification accuracy. Liu et al. [
16], using a second-order classification method based on covariance, classified sea-ice SAR images from the RADARSAT-2 satellite over Liaodong Bay.
Furthermore, a multitude of research scholars have proposed various polarization parameters for sea-ice classification [
17,
18,
19], and the reliability of these parameters has been widely recognized and applied within the academic community. For instance, Zhang et al. [
20] achieved accurate classification of Bohai Sea ice using H/α decomposition, Freeman decomposition, and polarization basis transformation features based on C-band RADARSAT-2 SAR data. Scheuchl et al. [
21] utilized full-polarization data in the C-band to extract polarization ratios and other polarization information for sea-ice type recognition in SAR images. Dabboor et al. [
22] developed a new SAR polarization feature for sea-ice classification using the coherence matrix of fully polarized SAR images. Zhang et al. [
23] describes a three-component scattering model to decompose PolSAR data of sea ice. The model is validated using C-band RADARSAT-2 quad-polarization data acquired over sea ice in the Bohai Sea. Many researchers have also devised various deep learning methods suitable for sea-ice classification [
24,
25], and these methods have demonstrated effective classification results in both polar regions.
Many of the aforementioned works have, to some extent, utilized SAR’s polarization information and explored the effects of different features on sea-ice classification. However, these works have been based on single-frequency SAR data and lack an in-depth study of the connection between the electromagnetic scattering characteristics of sea-ice targets and electromagnetic wave frequencies. In order to better understand the electromagnetic scattering characteristics of sea ice at different frequencies and to improve the accuracy and reliability of sea-ice classification, Rignot et al. [
26] conducted experiments on sea-ice classification using multi-frequency SAR data from the airborne AIRSAR system in the Beaufort Sea. The experimental results demonstrated that using multi-frequency polarimetric feature data can significantly improve sea-ice classification accuracy, with classification accuracy increasing by 14% to 20% compared to single-frequency, single-polarization data. Xie et al. [
27] analyzed and discussed the advantages and limitations of combining dual-band SAR polarimetric features for SAR sea-ice classification and recognition, using C- and L-band fully polarized sea-ice SAR data acquired by the SIR-C SAR system in the Weddell Sea, Antarctica. These studies collectively indicate that utilizing multi-dimensional SAR data for sea-ice classification is an effective means to enhance SAR sea-ice type recognition capabilities.
Many of the existing sea-ice classification studies mentioned above are primarily based on the freezing period. With variations in sea-ice age, ice thickness, SAR system wavelength, and incidence angle, the scattering mechanisms of sea ice also differ [
28]. Furthermore, during the melting period, the presence of surface meltwater on sea ice absorbs electromagnetic waves, making the scattering characteristics on the sea-ice surface complex and challenging to differentiate. This complexity leads to sea-ice classification methods and features that are effective during the freezing period often yielding unsatisfactory results during the melting period [
21].
Therefore, two main factors hinder the accurate classification of sea ice during the melting period. Firstly, there is already a wealth of polarization features available for sea-ice classification, but the classification capabilities of multi-frequency polarization features for sea ice during the melting period have not been fully explored. Secondly, although many classifiers have been applied to sea-ice classification, the impact of different combinations of classifiers and feature sets on sea-ice classification results remains uncertain.
To address the scientific questions mentioned above, this study contributes in the following three ways: First, we conducted, for the first time, an experiment to acquire multi-frequency, fully polarimetric PolSAR sea-ice data during the melting season in the Bohai Sea region. The experimental data include three frequency bands: L, S, and C. Second, we assessed the separability of polarimetric features for various sea-ice type combinations and analyzed the distinctions and connections among polarimetric features in different frequency bands. Third, we introduced a sea-ice classification method for the melting season based on multi-frequency and multi-polarization PolSAR polarimetric feature selection. We analyzed and discussed the impact of classifier–feature set combinations on classification results.
The study is organized as follows: In
Section 2, the data used in the study are introduced, including airborne multi-frequency PolSAR data, Sentinel-2 data, and a description of the process for interpreting sea-ice types during the melting period.
Section 3 summarizes the 51 polarimetric features used for sea-ice classification, explains how the best sea-ice classification parameters are chosen using Euclidean distance, and provides detailed information on the parameters of the selected classifiers. In
Section 4, the separability strength of polarimetric features in different sea-ice type combinations in each band is evaluated, and the reasons for these differences are analyzed.
Section 5 describes the classification results obtained with different combinations of classifiers and features. Finally,
Section 6 concludes the study and provides a discussion of the findings.
4. Feature Analysis
This section is divided into two parts. The first part evaluates the classification capabilities of polarimetric features in different combinations of sea-ice types across various frequency bands. The second part analyzes and compares the characteristics of polarimetric features in each frequency band. It is important to note that this section focuses on the discussion of the classification abilities of features that meet the classification requirements in various combinations of sea-ice types, and polarimetric features in each frequency band refer to the polarimetric features that meet these classification requirements in the respective bands.
4.1. Single-Frequency Polarimetric Feature Analysis
4.1.1. L-Band
In the L-band, there are a total of 12 polarimetric features with Ice Type values greater than or equal to 1.2, with SE showing the highest sea-ice classification capability, having an Ice Type value of 2.75. The specific values of ED for these 12 polarimetric features in different classification combinations can be found in
Figure 5, and the polarimetric feature images with the top three Ice Type values are displayed in
Figure 6.
Observing
Figure 6, it is evident that different types of sea ice exhibit distinct textures. Especially, the contrast between the rough surface of Gw and the smooth surface of Gi is quite clear. Using L-band polarization features provides an advantage in classifying sea ice with significant differences in surface roughness, which is consistent with previous research [
38]. As shown in
Figure 5, among these 10 type combinations, the A
ED values satisfying strong separability include OW–Gw, OW–GwW, Gi–Gw, Gi–GwW, GiW–Gw, and GiW–GwW. This indicates a relatively strong separability associated with Gw and GwW. However, L-band polarization features do not exhibit good separability in all sea-ice type combinations. For instance, the A
ED value for the Gw–GwW combination is 0.75, and none of the features meet the classification criteria, indicating poor separability between the two. Although Gw and GwW exhibit distinct brightness characteristics in optical imagery, in L-band SAR images, their backscattering coefficients are similar. The physical reason could be that GwW and Gw share similar physical structures, with rough surfaces and many cracks. Although GwW has some surface meltwater, it is minimal, and L-band electromagnetic waves are less sensitive to it.
While L-band polarization features exhibit strong separability in most sea-ice types, they perform poorly in separating thin ice (Gi and GiW) from open water (OW). The A
ED for OW–Gi and OW–GiW combinations in L-band polarization features is low, at only 0.30 and 0.87, respectively. The A
ED value for OW–Gi is the lowest among all type combinations, with all feature ED values below 1.2. This is because L-band frequencies are shorter, and electromagnetic waves have strong penetration capabilities [
39], while Gi is relatively thin. In the feature image, the numerical values for OW and Gi are very close, resulting in poor separability. The same reasoning can explain the poor separability between OW and GiW. In conclusion, L-band polarization features perform poorly in classifying thin ice types [
26,
40], but they perform well in combinations with significant differences in thickness and roughness, such as Gi–Gw, Gi–GwW, GiW–Gw, and GiW–GwW.
4.1.2. S-Band
In the S-band, there are a total of nine polarimetric features that meet the classification criteria, with SE
I exhibiting the highest ice classification capability, having an Ice Type value of 2.19. The specific ED values of the polarimetric features that meet the classification criteria in various classification combinations are shown in
Figure 7, and the polarimetric feature images with the top three Ice Type values are displayed in
Figure 8.
Observing
Figure 8a, it can be seen that seawater has the highest values in the Shannon entropy image, followed by Gi and GiW, while Gw and GwW have the lowest and closest values. Visually, there are distinct differences between seawater and sea ice. S-band polarization features perform well in the four combinations of sea ice and seawater (OW–Gi, OW–GiW, OW–Gw, OW–GwW). Specifically, the S-band polarization features meet the classification requirements for A
ED in these four combinations. Moreover, except for P
V-Yamaguchi and λ
3 in the OW–Gi combination, the ED values for S-band polarization features are greater than 1.2 in all four combinations.
Among the sea-ice type combinations, the AED values for S-band polarization features are less than 1.2 in the Gi–GiW, GiW–Gw, and Gw–GwW combinations. Particularly in the Gw–GwW combination, the ED values for all features are less than 1.2. But in the Gi–Gw, Gi–GwW and GiW–GwW combinations, the ED values for all features are more than 1.2. S-band performs better in the separability of thinner sea ice compared to other bands.
4.1.3. C-Band
In the C-band, there are a total of 12 polarimetric features that meet the classification criteria, with SE exhibiting the highest ice classification capability, having an Ice Type value of 2.92. The specific ED values of the polarimetric features that meet the classification criteria in various classification combinations are shown in
Figure 9, and the polarimetric feature images with the top three Ice Type values are displayed in
Figure 10.
Observing
Figure 10, we find that in the C-band polarization feature images, the numerical values for all types of sea ice are close, but there are significant differences compared to seawater. This is because the C-band radar wavelength is short, and seawater exhibits relatively high radar wave absorption in this frequency band. Seawater forms a strong contrasting state with sea ice, leading to clear differentiation between the two [
41].
From
Figure 9, we can see that the A
ED for all four combinations of sea ice and seawater is greater than 2 in the C-band. The numerical values in the C-band for these four combinations are higher than those in the S-band, indicating a stronger capability for distinguishing sea ice from seawater in the C-band. However, compared to other bands, C-band polarization features exhibit poorer separability among different sea-ice types. For example, in the Gi–GiW, Gi–Gw, and GiW–Gw combinations, the A
ED values for the selected polarization features are all less than 1. Particularly in the Gi–GiW combination, the A
ED is only 0.52, the lowest among all combinations, and only
has an ED value greater than 1.2 in this combination. However, C-band features perform well in the Gi–GwW, GiW–GwW, and Gw–GwW combinations, with A
ED values all exceeding 1.2. Considering the A
ED value for the OW–GwW combination, we can conclude that in the C-band, GwW exhibits strong separability from other sea-ice types.
In summary, the L-band polarization features exhibit strong separability when dealing with sea-ice types related to Gw and GwW. However, their ability to distinguish between seawater and sea ice, especially in combinations with thin ice, is relatively weak. The S-band polarization features perform better in distinguishing between seawater and sea ice compared to the L-band but not as effectively as the C-band. They excel in separability, particularly in the case of thin ice. On the other hand, C-band polarization features show strong separability between seawater and sea ice, but there is still room for improvement in separability among different sea-ice types. In conclusion, the L-band is suitable for sea-ice type classification, the S-band is suitable for identifying thin ice types, and the C-band is suitable for sea-ice–water classification. This is consistent with the conclusion proposed by Dierking W, suggesting that higher-frequency electromagnetic waves are more reliable for sea-ice classification during the melting and freezing periods [
42]. It is evident that the frequency of electromagnetic waves has a significant impact on the separability of sea-ice types.
4.2. Analysis and Comparison of Multi-Frequency Features
In order to analyze the properties of polarization features in each frequency band, we have compiled a total of 15 polarization features that meet the classification requirements. These features are as follows: λ1, λ2, λ3, P1, H, λ, , α1, SE, SEI, SEP, PV-Freeman, PV-Yamaguchi, C22, and Span.
Through an analysis of the scattering mechanisms of the aforementioned polarization features, they can be broadly categorized into three groups:
- (1)
Total power parameters (λ, SE, SEI, SEP, Span);
- (2)
Volume scattering parameters (PV-Freeman, PV-Yamaguchi);
- (3)
Scattering mechanism parameters (λ1, λ2, λ3, P1, H, , α1, C22).
In the three bands, there are seven common features that exhibit strong discriminative capabilities. These features are SE, SE
I, Span, λ
1, λ
2, λ
3, and P
V-Freeman. When ranking the discriminative capabilities of these features based on their Ice Type values in each frequency band, the top three features are consistently SE, SE
I, and Span. This is in line with the results discussed by Zhao et al. [
43] regarding the importance of polarimetric features in sea-ice classification. The order of these features varies slightly between different frequency bands (with SE > SE
I > Span in L-band and C-band, and SE
I > SE > Span in the S-band). This suggests that features related to total power play a significant role in the classification of melting sea ice. The physical properties and structures of sea ice are closely related to their interaction with microwave signals, which are reflected in features related to power. Additionally, these parameters have a relatively lower dimensionality, making them simpler and more manageable in the feature space, facilitating easier processing and analysis.
The eigenvalues (λi (i = 1,2,3)) in the scattering mechanism parameters not only reflect the intensity of different scattering mechanisms on the Earth’s surface but also capture all the scattering information in the received echoes. Rich information can provide better classification capabilities, and the calculation of many scattering mechanism parameters is related to the eigenvalues λi, highlighting the significance of these eigenvalues.
Although the scattering mechanisms of sea ice typically involve surface scattering and volume scattering, we observed that the surface scattering parameters in the three bands do not show strong sea-ice classification ability. Furthermore, in the case of sea ice during the melting period, the classification performance of volume scattering parameters was significantly better than that of surface scattering parameters. This is attributed to the presence of meltwater on the ice surface during the melting process, which enhances microwave absorption and, in turn, complicates surface scattering characteristics, making them more difficult to distinguish [
44]. In contrast, volume scattering parameters typically reflect the structural features inside the target, making them more suitable for distinguishing sea ice during the melting period. The two volume scattering parameters selected in this study are derived from Freeman decomposition and Yamaguchi decomposition. In all three bands, the separability of the volume scattering component features obtained from Freeman decomposition exceeded those obtained from Yamaguchi decomposition. This is because the volume scattering modeling in Freeman decomposition follows a random distribution, resulting in a more uniform numerical distribution of sea ice. In contrast, the volume scattering model in Yamaguchi decomposition is based on a sinusoidal distribution and introduces new helical scattering. Therefore, in the sea-ice data during the melting period in this experiment, the volume scattering energy in Freeman decomposition is greater than that in Yamaguchi decomposition, making the differences in volume scattering between different types of sea ice more pronounced and enhancing separability [
45].
The above seven polarimetric features perform well in all three bands, indicating that these features are not significantly affected by the differences in electromagnetic wave frequencies, or the impact is relatively low when it comes to sea-ice classification.
The alpha approximation (
) demonstrates good separability in both the L-band and the C-band. The calculation formula for
is closely related to parameters P
1 and α
1 [
17], and these three parameters exhibit strong separability in the L-band. The stronger the difference in the shape of sea ice, the better the separability of
. This is consistent with its performance in the separability of type combinations in the L-band and C-band, such as in the L-band, where
has A
ED values of 2.06 and 3.63 in OW–Gw and GiW–Gw combinations, respectively. Additionally, due to the different information acquisition capabilities of the L-band and C-band in distinguishing sea ice from seawater,
exhibits strong classification abilities in both bands.
Scattering entropy (H) reflects the randomness of the scattering return from the target and the structure of the target. Similar to , the greater the difference in the structure of the target, the stronger the separability. Compared to other bands, L-band electromagnetic waves can provide more information about the internal structure of sea ice, which is why the L-band scattering entropy exhibits good classification capabilities among different sea-ice types.
C
22 exhibits good separability in the C-band. C
22 represents the amplitude information of the backscattering signal in VV polarization mode, reflecting the intensity and scattering characteristics of VV polarization waves in SAR images. The greater the depolarization of backscattering from sea ice, the easier it is to distinguish it from water because water’s backscattering is mainly influenced by surface scattering, resulting in typically lower cross-polarized responses [
12]. It is precisely because C
22 exhibits strong separability in combinations of sea ice and seawater, thus meeting the classification requirements.
7. Discussion and Conclusions
In this study, we proposed an ice classification method based on multi-dimensional SAR data feature selection. The new method was applied to the Bohai Sea and successfully classified OW, Gi, GiW, Gw, and GwW during the ice melting period.
To obtain multi-frequency, full-polarization PolSAR ice data during the ice melting season, the research team conducted a series of flight experiments over the Bohai Sea on 27–28 February 2022. They used a Modern Ark 60 aircraft equipped with a multi-dimensional SAR system and successfully acquired PolSAR data in the L-band, S-band, and C-band. This marked the first time that airborne SAR data were collected during the ice melting season in the Bohai Sea, providing valuable multi-frequency polarization data for ice research.
As the foundation of classification, the selection of polarization features is crucial. Firstly, we calculated the Euclidean distances between different types of sea ice and assessed the separability of 51 polarization features in the L-band, S-band, and C-band. The following conclusions were drawn: the L-band is suitable for ice type recognition, the S-band is suitable for classifying thin ice, and the C-band is suitable for ice–water classification. The results demonstrate that the L-band has 12 features, the S-band has 9 features, and the C-band has 12 features, totaling 33 polarization features with strong separability. Among them, SE, SEI, and Span have shown outstanding performance in all three bands. Additionally, there are seven common polarization features that exhibit strong classification abilities in all three bands, and it is evident that volume scattering parameters are more suitable for classifying ice during the melting season than surface scattering parameters.
Furthermore, we constructed the eligible polarization features into polarization feature sets for each frequency band. Combining the RFE method, we input the feature sets into the SVM classifier and obtained classification results for different feature counts in each band. The results revealed that in the C-band, OW and Gi had the highest classification accuracy, GiW achieved the highest classification accuracy in the S-band, while Gw and GwW had the highest classification accuracy in the L-band. This can be attributed to the strong electromagnetic wave absorption of seawater at low frequencies. In the L-band images, the backscatter coefficient of seawater is lower, approaching the base noise level, resulting in lower classification accuracy for OW. Sea ice usually has a rough surface, and the L-band has better electromagnetic wave penetration capability, providing richer information about the internal structure of sea ice. Therefore, it achieves higher classification accuracy for rough sea-ice types like Gw and GwW, which is consistent with the findings in [
35,
36].
Finally, we utilized the SVM classifier to obtain single-frequency classification results and discussed the identification of the optimal single-frequency polarization feature set. We constructed a multi-dimensional polarization feature set by combining the optimal polarization features from the L-band, S-band, and C-band. Using the RFE method, we input these feature sets into four different machine learning classifiers. By comparing the ice classification accuracy at different feature counts, we discussed the composition of the optimal multi-dimensional SAR polarization feature set for different classifiers. In the case of using the SVM classifier, the multi-dimensional polarization feature set exhibited improved classification accuracy compared to the three single-frequency polarization feature sets, with improvements ranging from 9% to 22%. The highest classification accuracy among different feature–classifier combinations was achieved when using the RF classifier at 95.84%. We validated our proposed method using verification data, and the results similarly demonstrated that our method is effective for classifying sea-ice types OW, Gi, GiW, Gw, and GwW during the melting season in the Bohai Sea.
According to the experimental results in this paper, we observed that the SVM classifier achieves higher classification accuracy when using a single-band feature set. In this case, the feature set contains fewer features with strong data correlation, making SVM more robust in handling the data. When using a multi-band feature set, which contains more features than the single-band feature set and is more complex, the RF classifier’s accuracy is superior to other models. It is mentioned that RF is more suitable for handling complex datasets, which aligns with the conclusions drawn in our paper.
We verify the generalization performance of the model by using four-scene validation data. The results of the validation indicated that the classification accuracy exceeded 89.74% across various scenarios. Additionally, we simulated Sentinel-1’s HH + HV dual-polarization data using C-band data and ALOS-2’s HH + HV dual-polarization data using L-band data. The results demonstrate that the comprehensive use of full-polarization data is superior to using only dual-polarization data. The comparison with previous studies on C-band data further validates the feasibility of using multi-band data for sea-ice classification during the melting period.
It might be challenging to obtain multi-band synchronous satellite data for sea-ice classification. However, in regions with high-frequency satellite coverage, such as polar areas, it is possible to leverage data from multiple satellites to achieve near-real-time sea-ice classification. Sea-ice melting is a slow process, and with the continuous increase in satellite data in the future, a coordinated approach using near-real-time data from multiple satellites is a possibility. This study focuses on exploring the classification capabilities of different bands in sea-ice classification, and the conclusions drawn from single-band data can be applied to satellite data. The conclusions obtained from the multi-frequency data can be used as reference for the subsequent development of multi-frequency satellites.
In conclusion, the novel sea-ice classification method based on PolSAR observation data feature selection proposed in this study has effectively classified sea ice during the melting season in the Bohai Sea. Currently, many deep learning models are applied to sea-ice classification, and these models often require a large amount of data for support to achieve good results. However, obtaining synchronized data for multi-band full polarization is challenging, and the dataset in this study may be insufficient to support the use of such networks. The future direction of improvement for this work involves accumulating a large amount of multi-band data. This can be achieved by conducting more airborne SAR flight experiments while simultaneously collecting near-real-time data from areas with high-frequency satellite coverage, including data from C-band, L-band, and S-band satellites. Applying airborne remote sensing methods to satellite remote sensing can be beneficial for improving the accuracy of sea-ice classification during the melting period.