Gini Coefficient-Based Feature Learning for Unsupervised Cross-Domain Classification with Compact Polarimetric SAR Data
Abstract
:1. Introduction
- (1)
- We extend the polarimetric decomposition method of specific CP modes to that of GCP mode and extract the GCP decomposition parameters to provide more abundant CP information for CP SAR cross-domain classification.
- (2)
- This study comprehensively explores the potential of multi-mode GCP SAR data in cross-domain classification and realizes the stable description of targets in different domains by GCP features. Furthermore, based on the proposed method, we extract optimal CP feature parameters that contribute to the feature classification effect of both the source and target domains and enhance the alignment effect of feature parameters in domain adaptation methods.
- (3)
- Based on the proposed stable and robust method, four types of unsupervised cross-domain image classification are carried out. This includes cross-domain classification of GCP data from different sensors over the same area, GCP data acquired over different areas, FP + GCP data over different areas, and FP + GCP data over different crop types.
- (4)
- In the land-cover classification of PolSAR, most of the studies only consider the FP information without considering the partial polarimetric information, such as the CP information. To the best of our knowledge, this is the first work in which we combine FP and CP features for cross-domain classification and realize high-precision cross-data source, cross-scene, and cross-crop type image classification based on the multi-mode GCP SAR and FP + GCP SAR features, respectively.
2. Study Area and Data Collection
2.1. Study Area
2.2. Data Collection
3. Methodology
3.1. FP/GCP SAR Feature Extraction
3.1.1. GCP SAR Data
3.1.2. GCP Decomposition Parameters
- (1)
- GCP H/α decomposition
- (2)
- GCP m-χ and m-δ decomposition
- (3)
- GCP m-αs decomposition
3.1.3. FP SAR Feature Parameters
3.2. GFRST-UDA Method
4. Results and Discussion
4.1. Cross-Domain Image Classification Based on the GCP SAR Images
4.1.1. Images from Different Sensors over the Same Area
- (1)
- GFRST feature selection
- (2)
- Evaluation of cross-domain classification results based on the GFRST-UDA method
4.1.2. Images Acquired over Different Areas
4.2. Cross-Domain Image Classification Based on the FP + GCP SAR Images
4.2.1. Cross-Scene Image Classification
4.2.2. Cross-Crop Type Image Classification
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
References
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Data Acquisition (D/M/Y) | SAR Frequency Band | Pixel Spacing (A × R, m) | Center Incidence Angle (°) | Study Area | Satellite Sensor |
---|---|---|---|---|---|
9 April 2008 | C band | 4.73 × 4.82 | 28.92 | San Francisco, CA, USA | RADARSAT-2 |
11 November 2009 | L band | 9.37 × 3.54 | 23.87 | San Francisco, CA, USA | ALOS-1 PALSAR-1 |
24 March 2015 | L band | 2.86 × 3.21 | 33.88 | San Francisco, CA, USA | ALOS-2 PALSAR-2 |
27 March 2018 | C band | 2.25 × 5.79 | 31.35 | San Francisco, CA, USA | GF-3 |
20 September 2017 | C band | 4.50 × 5.00 | 48.75 | Qingdao, China | GF-3 |
16 September 2015 | C band | 5.20 × 7.60 | 39.95 | Jiangsu, China | RADARSAT-2 |
31 March 2023 | C band | 4.73 × 4.74 | 26.70 | Yellow River, China | RADARSAT-2 |
Parameter Extraction Method | Feature Component | Feature Number |
---|---|---|
Stokes matrix | g0, g1, g2, g3 | 4 |
Backscattering intensity | σCV, σCR, σCH, σCL [14] | 4 |
m-δ decomposition | Ps_m-δ, Pd_m-δ, Pv_m-δ | 3 |
m-χ decomposition | Ps_m-χ, Pd_m-χ, Pv_m-χ | 3 |
m-αs decomposition | Ps_m-αs, Pd_m-αs, Pv_m-αs | 3 |
H/α decomposition | H, α, A | 3 |
ΔαBCP/αBCP decomposition | ΔαBCP, αBCP [8] | 2 |
Total | - | 22 |
Parameter Extraction Method | Feature Component | Feature Number |
---|---|---|
H/α decomposition | H_Full, α_Full, A_Full | 3 |
Freeman decomposition | Ps_F, Pd_F, Pv_F | 3 |
ΔαB/αB decomposition [46] | αB, ΔαB, Φ | 3 |
Yamaguchi decomposition [47] | Ps_Y, Pd_Y, Pv_Y, Ph_Y | 4 |
Pauli decomposition | Pauli_1, Pauli_2, Pauli_3 | 3 |
Polarization characteristics of the rotating domain [48] | Re[T12(θnull)], Im[T12(θnull)], Re[T23(θnull)] | 3 |
Polarimetric coherency matrix | T11, T22, T33 | 3 |
Total | - | 22 |
Experimental Transfer Group 1 | Experimental Transfer Group 2 | Experimental Transfer Group 3 | Experimental Transfer Group 4 | ||||
---|---|---|---|---|---|---|---|
Source Domain→Target Domain | Source Domain→Target Domain | Source Domain→Target Domain | Source Domain→Target Domain | ||||
a | ALOS1-Sanf→RS2-Sanf | a | GF3-Qingdao→RS2-Sanf | a | GF3-Qingdao→RS2-Sanf | a | GF3-Qingdao→RS-2 Yellow River |
b | ALOS2-Sanf→RS2-Sanf | b | GF3-Qingdao→ALOS1-Sanf | b | GF3-Qingdao→ALOS1-Sanf | b | RS2-Qingdao→RS-2 Yellow River |
c | GF3-Sanf→RS2-Sanf | c | GF3-Qingdao→ALOS2-Sanf | c | GF3-Qingdao→ALOS2-Sanf | c | Jiangsu T-H→RS-2 Yellow River |
d | RS2-Sanf→ALOS1-Sanf | d | GF3-Qingdao→GF3-Sanf | d | GF3-Qingdao→GF3-Sanf | d | Jiangsu D-J→RS-2 Yellow River |
e | ALOS2-Sanf→ALOS1-Sanf | e | ALOS2-Sanf→GF3-Qingdao | e | RS2-Sanf→GF3-Qingdao | - | |
f | GF3-Sanf→ALOS1-Sanf | - | f | ALOS1-Sanf→GF3-Qingdao | - | ||
g | RS2-Sanf→ALOS2-Sanf | - | g | ALOS2-Sanf→GF3-Qingdao | - | ||
h | ALOS1-Sanf→ALOS2-Sanf | - | h | GF3-Sanf→GF3-Qingdao | - | ||
i | GF3-Sanf→ALOS2-Sanf | - | - | - | |||
j | RS2-Sanf→GF3-Sanf | - | - | - | |||
k | ALOS1-Sanf→GF3-Sanf | - | - | - | |||
l | ALOS2-Sanf→GF3-Sanf | - | - | - |
RS2→ALOS1 | RS2→ALOS2 | RS2→GF3 | ALOS1→ALOS2 | ALOS1→GF3 | ALOS2→GF3 | |
---|---|---|---|---|---|---|
Feature Number | 13 | 11 | 13 | 13 | 11 | 11 |
Optimal parameters | Pd_m-δ, g1, σCL, Pd_m-χ, Pd_m-αs, g0, αBCP, σCR, α, σCH, Pv_m-αs, Pv_m-δ, Pv_m-χ | Pd_m-δ, g1, σCL, Pd_m-χ, Pd_m-αs, g0, σCR, σCH, Pv_m-αs, Pv_m-δ, Pv_m-χ | g1, σCL, Pd_m-αs, g0, αBCP, σCR, α, σCH, Pv_m-αs, Pv_m-δ, Pv_m-χ, H, A | Ps_m-αs, Pd_m-αs, Pd_m-χ, Pd_m-δ, g1, σCV, σCL, σCR, g0, Pv_m-δ, σCH, Pv_m-χ, Pv_m-αs | αBCP, α, Pd_m-αs, g1, σCL, σCR, g0, Pv_m-δ, σCH, Pv_m-χ, Pv_m-αs | Ps_m-δ, g1, Ps_m-χ, Pd_m-αs, Pv_m-αs. Pv_m-δ σCR. Pv_m-χ, σCL, g0, σCH |
ALOS1→RS2 | ALOS2→RS2 | GF3→RS2 | RS2→ALOS1 | ALOS2→ALOS1 | GF3→ALOS1 | RS2→ALOS2 | ALOS1→ALOS2 | GF3→ALOS2 | RS2→GF3 | ALOS1→GF3 | ALOS2→GF3 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
GFRST-SA | 0.938 | 0.961 | 0.935 | 0.832 | 0.958 | 0.821 | 0.895 | 0.948 | 0.726 | 0.945 | 0.914 | 0.804 |
GFRST-TCA | 0.936 | 0.968 | 0.935 | 0.842 | 0.940 | 0.882 | 0.879 | 0.905 | 0.863 | 0.945 | 0.931 | 0.779 |
GFRST-JDA | 0.943 | 0.963 | 0.949 | 0.847 | 0.947 | 0.835 | 0.953 | 0.951 | 0.926 | 0.940 | 0.917 | 0.940 |
GFRST-CORAL | 0.922 | 0.956 | 0.934 | 0.793 | 0.957 | 0.807 | 0.797 | 0.959 | 0.795 | 0.950 | 0.921 | 0.782 |
GFRST-BDA | 0.943 | 0.965 | 0.949 | 0.847 | 0.947 | 0.836 | 0.839 | 0.951 | 0.926 | 0.940 | 0.917 | 0.940 |
GFRST-GFK | 0.940 | 0.956 | 0.940 | 0.831 | 0.959 | 0.822 | 0.894 | 0.949 | 0.728 | 0.942 | 0.914 | 0.803 |
GFRST-MEDA | 0.944 | 0.968 | 0.970 | 0.841 | 0.946 | 0.930 | 0.967 | 0.942 | 0.960 | 0.937 | 0.921 | 0.939 |
Supervised classification | 0.987 | 0.977 | 0.944 | 0.979 |
GF3-Qingdao→RS2-Sanf | GF3-Qingdao→ALOS1-Sanf | GF3-Qingdao→ALOS2-Sanf | GF3-Qingdao→GF3-Sanf | ALOS2-Sanf→GF3-Qingdao | |
---|---|---|---|---|---|
GFRST-SA | 0.913 | 0.957 | 0.884 | 0.899 | 0.908 |
GFRST-TCA | 0.881 | 0.942 | 0.872 | 0.851 | 0.920 |
GFRST-JDA | 0.937 | 0.920 | 0.914 | 0.898 | 0.935 |
GFRST-CORAL | 0.839 | 0.948 | 0.896 | 0.891 | 0.524 |
GFRST-BDA | 0.930 | 0.934 | 0.900 | 0.901 | 0.913 |
GFRST-GFK | 0.878 | 0.922 | 0.879 | 0.889 | 0.867 |
GFRST-MEDA | 0.901 | 0.915 | 0.869 | 0.874 | 0.914 |
Supervised classification | 0.987 | 0.977 | 0.944 | 0.979 | 0.980 |
GF3 Qingdao→RS2 Sanf | GF3 Qingdao→ALOS1-Sanf | GF3 Qingdao→ALOS2 Sanf | GF3 Qingdao→GF3-Sanf | RS2 Sanf→GF3 Qingdao | ALOS1 Sanf→GF3 Qingdao | ALOS2 Sanf→GF3 Qingdao | GF3 Sanf→GF3 Qingdao | |
---|---|---|---|---|---|---|---|---|
SA | 0.886 | 0.948 | 0.912 | 0.874 | 0.784 | 0.958 | 0.976 | 0.795 |
GFRST-SA | 0.972 | 0.970 | 0.962 | 0.942 | 0.970 | 0.980 | 0.980 | 0.982 |
CORAL | 0.831 | 0.852 | 0.897 | 0.844 | 0.794 | 0.937 | 0.949 | 0.802 |
GFRST-CORAL | 0.967 | 0.937 | 0.937 | 0.942 | 0.979 | 0.980 | 0.981 | 0.984 |
JDA | 0.933 | 0.960 | 0.932 | 0.928 | 0.885 | 0.983 | 0.980 | 0.908 |
GFRST-JDA | 0.967 | 0.987 | 0.985 | 0.941 | 0.979 | 0.986 | 0.985 | 0.979 |
GFK | 0.900 | 0.930 | 0.910 | 0.868 | 0.798 | 0.953 | 0.979 | 0.768 |
GFRST-GFK | 0.971 | 0.958 | 0.943 | 0.952 | 0.966 | 0.979 | 0.982 | 0.983 |
BDA | 0.931 | 0.936 | 0.939 | 0.922 | 0.958 | 0.976 | 0.978 | 0.970 |
GFRST-BDA | 0.973 | 0.990 | 0.987 | 0.928 | 0.975 | 0.983 | 0.990 | 0.988 |
MEDA | 0.912 | 0.969 | 0.935 | 0.912 | 0.923 | 0.976 | 0.991 | 0.897 |
GFRST-MEDA | 0.985 | 0.995 | 0.997 | 0.957 | 0.990 | 0.986 | 0.991 | 0.993 |
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Guo, X.; Yin, J.; Li, K.; Yang, J. Gini Coefficient-Based Feature Learning for Unsupervised Cross-Domain Classification with Compact Polarimetric SAR Data. Agriculture 2024, 14, 1511. https://doi.org/10.3390/agriculture14091511
Guo X, Yin J, Li K, Yang J. Gini Coefficient-Based Feature Learning for Unsupervised Cross-Domain Classification with Compact Polarimetric SAR Data. Agriculture. 2024; 14(9):1511. https://doi.org/10.3390/agriculture14091511
Chicago/Turabian StyleGuo, Xianyu, Junjun Yin, Kun Li, and Jian Yang. 2024. "Gini Coefficient-Based Feature Learning for Unsupervised Cross-Domain Classification with Compact Polarimetric SAR Data" Agriculture 14, no. 9: 1511. https://doi.org/10.3390/agriculture14091511