PolSAR Image Land Cover Classification Based on Hierarchical Capsule Network
Abstract
:1. Introduction
- (1)
- The HCapsNet is proposed for land cover classification of PolSAR images. It can simultaneously consider the deep features obtained at different network levels, which describes the polarimetric scattering information of land covers more comprehensively with small training sample size, and significantly reduces the misclassification of class boundaries.
- (2)
- The CRF is combined with the HCapsNet to further refine the classification results. The intra-class misclassifications can be reduced by the spatial information constraints of the CRF.
- (3)
- We adopt three discriminative attributes for land covers of PolSAR data, i.e., phase, amplitude, and polarimetric decomposition, to uniformly describe the scattering mechanism of land covers with different sensors, bands, and resolutions. Moreover, the generalization performance of the proposed method is verified to be better than other comparison methods.
2. Methodology
2.1. Polarimetric Feature Extraction
2.2. Construction of the Primary Capsule Layer
2.3. Construction of the Higher-Level Capsule Layer
2.4. Conditional Random Field
2.5. Implementation Details
Algorithm 1 Proposed method |
|
3. Experiment Results and Analysis
3.1. Data Description and Parameter Settings
3.2. Classification Results of the AIRSAR Flevoland Dataset
3.3. Classification Results of the AIRSAR San Francisco Dataset
3.4. Classification Results of the GF-3 Dataset
3.5. Analysis of the Performance
4. Discussion
4.1. Contributions of Polarimetric Features
4.2. Comparison of Different Feature Extractors
4.3. Effect of the CRF
4.4. Generalization Performance
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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H | A | ||||||
phase | amplitude | polarimetric decomposition |
Platform | Windows 10 |
Keras/TensorFlow | V 2.2.4/V 1.13.1 |
CPU | Intel Core i7-10700K |
Memory | 16 G |
GPU | Nvidia GeForce RTX 2080 SUPER |
Video memory | 8 G |
Class | Name | Train | Test | Total |
---|---|---|---|---|
1 | Stem bean | 1.00% | 99.00% | 8764 |
2 | Rapeseed | 1.00% | 99.00% | 19,326 |
3 | Bare soil | 1.01% | 98.99% | 5340 |
4 | Potatoes | 1.00% | 99.00% | 17,758 |
5 | Wheat | 1.00% | 99.00% | 17,636 |
6 | Wheat2 | 1.00% | 99.00% | 10,371 |
7 | Peas | 1.01% | 98.99% | 10,417 |
8 | Wheat3 | 1.00% | 99.00% | 22,090 |
9 | Lucerne | 1.00% | 99.00% | 10,967 |
10 | Barely | 1.01% | 98.99% | 8601 |
11 | Grasses | 1.00% | 99.00% | 8365 |
12 | Beets | 1.00% | 99.00% | 10,161 |
13 | Buildings | 1.11% | 98.89% | 904 |
14 | Water | 1.01% | 98.99% | 3477 |
15 | Forest | 1.00% | 99.00% | 22,841 |
Total | 1.00% | 99.00% | 177,018 |
Class | Name | Train | Test | Total |
---|---|---|---|---|
1 | High-density urban | 0.50% | 99.50% | 163,370 |
2 | Vegetation | 0.50% | 99.50% | 157,698 |
3 | Water | 0.50% | 99.50% | 332,252 |
4 | Developed urban | 0.50% | 99.50% | 110,918 |
5 | Low-density urban | 0.51% | 99.49% | 12,263 |
Total | 0.50% | 99.50% | 776,501 |
Class | Name | Train | Test | Total |
---|---|---|---|---|
1 | Bare soil | 0.21% | 99.80% | 23,038 |
2 | Forest | 0.20% | 99.79% | 39,099 |
3 | Cole | 0.20% | 99.80% | 49,588 |
4 | Wheat | 0.20% | 99.80% | 35,121 |
5 | Grasses | 0.21% | 99.79% | 10,214 |
6 | Water | 0.20% | 99.80% | 5382 |
7 | Sand | 0.21% | 99.79% | 10,213 |
8 | Wetland | 0.56% | 99.44% | 893 |
Total | 0.20% | 99.80% | 173,550 |
Class | 1D-CNN | 2D-CNN | DenseNet | CapsNet | CapsNet-3 | Proposed Method |
---|---|---|---|---|---|---|
Steam bean | 94.57 | 99.23 | 99.48 | 99.10 | 99.15 | 99.29 |
Rapeseed | 89.77 | 95.32 | 95.31 | 96.16 | 98.15 | 98.84 |
Bare soil | 98.01 | 99.56 | 98.15 | 99.85 | 99.92 | 100.00 |
Potatoes | 86.52 | 96.55 | 95.37 | 99.45 | 99.24 | 99.72 |
Wheat | 90.84 | 96.86 | 95.83 | 98.57 | 97.16 | 99.84 |
Wheat2 | 83.68 | 96.27 | 83.14 | 96.39 | 96.26 | 98.28 |
Peas | 92.35 | 93.64 | 92.72 | 96.52 | 97.03 | 98.30 |
Wheat3 | 96.24 | 99.85 | 99.90 | 99.75 | 99.96 | 99.98 |
Lucerne | 92.60 | 96.16 | 96.41 | 95.43 | 95.31 | 97.08 |
Barley | 91.91 | 98.39 | 98.04 | 99.74 | 99.90 | 99.87 |
Grasses | 69.11 | 97.30 | 96.51 | 99.29 | 99.08 | 99.12 |
Beets | 89.92 | 93.84 | 95.90 | 96.19 | 96.05 | 97.19 |
Buildings | 68.36 | 92.17 | 6.97 | 74.50 | 88.37 | 91.92 |
Water | 64.91 | 88.99 | 56.57 | 88.03 | 88.52 | 99.57 |
Forest | 89.15 | 98.43 | 96.32 | 94.91 | 96.88 | 99.15 |
OA | 89.27 | 96.91 | 94.58 | 97.31 | 97.74 | 99.04 |
AA | 86.53 | 96.17 | 87.11 | 95.59 | 96.73 | 98.54 |
Kappa | 0.8824 | 0.9665 | 0.9406 | 0.9754 | 0.9783 | 0.9895 |
Class | 1D-CNN | 2D-CNN | DenseNet | CapsNet | CapsNet-3 | Proposed Method |
---|---|---|---|---|---|---|
High-density urban | 82.48 | 86.62 | 97.37 | 86.51 | 83.50 | 92.62 |
Vegetation | 91.49 | 92.83 | 85.14 | 90.56 | 96.64 | 97.32 |
Water | 99.56 | 97.70 | 99.94 | 99.80 | 99.64 | 99.96 |
Developed urban | 74.10 | 88.13 | 85.47 | 94.83 | 95.23 | 97.84 |
Low-density urban | 0.00 | 0.00 | 0.00 | 17.38 | 7.22 | 33.28 |
OA | 89.12 | 91.47 | 92.75 | 93.12 | 93.54 | 96.52 |
AA | 69.52 | 73.06 | 73.58 | 77.82 | 76.44 | 84.20 |
Kappa | 0.8460 | 0.8799 | 0.8974 | 0.9032 | 0.9091 | 0.9510 |
Class | 1D-CNN | 2D-CNN | DenseNet | CapsNet | CapsNet-3 | Proposed Method |
---|---|---|---|---|---|---|
Bare soil | 94.39 | 97.43 | 99.62 | 96.78 | 97.5 | 99.09 |
Forest | 98.85 | 99.96 | 99.86 | 99.99 | 99.98 | 100 |
Cole | 99.59 | 99.49 | 99.84 | 98.63 | 99.33 | 99.91 |
Wheat | 99.47 | 98.99 | 92.34 | 99.1 | 99.28 | 99.99 |
Grasses | 24.39 | 65.58 | 23.96 | 85.17 | 80.78 | 88.77 |
Water | 64.68 | 66.09 | 76.66 | 78.04 | 80.96 | 85.97 |
Sand | 69.58 | 98.23 | 75.31 | 93.81 | 95.56 | 99.58 |
Wetland | 47.48 | 85.44 | 39.87 | 93.06 | 96.53 | 95.74 |
OA | 91.16 | 96.04 | 91.36 | 97.04 | 97.32 | 98.71 |
AA | 74.8 | 88.9 | 75.93 | 93.07 | 93.74 | 96.13 |
Kappa | 0.8886 | 0.9504 | 0.8914 | 0.963 | 0.9665 | 0.9838 |
2D-CNN | CNN-CapsNet | HCapsNet (without CRF) | |
---|---|---|---|
Layer 1 | Input. 8 @ 15×15 | Input. 8 @ 15×15 | Input. 8 @ 15×15/“Reshape” |
Layer 2 | Conv. 128 @ 3×3 | Conv. 128 @ 3×3 | Conv. 128 @ 3×3/“Reshape” |
Layer 3 | Conv. 256 @ 3×3 | Conv. 256 @ 3×3 | Conv. 256 @ 3×3/“Reshape” |
Layer 4 | Max_pooling/Flatten | “Reshape” | Concatenate |
Layer 5 | Fully Connected | Primary Capsule | Primary Capsule |
Layer 6 | Dropout | Higher-level Capsule | Higher-level Capsule |
Layer 7 | Softmax | Length | Length |
Method | Sample Rate | OA | AA | Kappa |
---|---|---|---|---|
MCCNN [52] | 1% | 95.83% | 96.02% | / |
Compact and Adaptive CNNs [57] | 1% | 96.35% | / | / |
CK-HDRF [58] | 1% | 96.75% | 97.00% | 0.9569 |
RCV-CNN [13] | 1% | 97.22% | 95.99% | 0.8930 |
HCapsNet without CRF | 1% | 98.34% | 97.44% | 0.9818 |
HCapsNet with CRF | 1% | 99.04% | 98.54% | 0.9895 |
Data Set | Accuracy | Dense- CapsNet | Res- CapsNet | CNN- CapsNet | HCapsNet (without CRF) | HCapsNet (with CRF) |
---|---|---|---|---|---|---|
AIRSAR | OA | 97.40 | 97.67 | 97.88 | 98.34 | 99.04 |
Flevoland | AA | 95.45 | 96.52 | 96.21 | 97.44 | 98.54 |
dataset | Kappa | 0.9715 | 0.9745 | 0.9769 | 0.9818 | 0.9895 |
AIRSAR | OA | 91.44 | 93.89 | 93.88 | 94.34 | 96.52 |
San Francisco | AA | 73.08 | 75.75 | 76.11 | 82.52 | 84.20 |
dataset | Kappa | 0.8796 | 0.9136 | 0.9135 | 0.9204 | 0.9510 |
GF-3 | OA | 93.45 | 96.58 | 97.12 | 97.86 | 98.71 |
Hulunbuir | AA | 80.57 | 94.50 | 90.87 | 94.51 | 96.13 |
dataset | Kappa | 0.9176 | 0.9575 | 0.9641 | 0.9731 | 0.9838 |
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Cheng, J.; Zhang, F.; Xiang, D.; Yin, Q.; Zhou, Y.; Wang, W. PolSAR Image Land Cover Classification Based on Hierarchical Capsule Network. Remote Sens. 2021, 13, 3132. https://doi.org/10.3390/rs13163132
Cheng J, Zhang F, Xiang D, Yin Q, Zhou Y, Wang W. PolSAR Image Land Cover Classification Based on Hierarchical Capsule Network. Remote Sensing. 2021; 13(16):3132. https://doi.org/10.3390/rs13163132
Chicago/Turabian StyleCheng, Jianda, Fan Zhang, Deliang Xiang, Qiang Yin, Yongsheng Zhou, and Wei Wang. 2021. "PolSAR Image Land Cover Classification Based on Hierarchical Capsule Network" Remote Sensing 13, no. 16: 3132. https://doi.org/10.3390/rs13163132