Crop Classification Using MSCDN Classifier and Sparse Auto-Encoders with Non-Negativity Constraints for Multi-Temporal, Quad-Pol SAR Data
Abstract
:1. Introduction
- (1)
- (2)
- Algorithms based on the scattering mechanisms of polarization [28]. The points with the same physical meaning are classified using the polarization scattering parameters obtained by the coherent and incoherent decomposition algorithms (such as Pauli decomposition [29], Freeman decomposition [30], etc.) [31,32,33,34,35];
- (3)
2. Methodology
2.1. PolSAR Data Structure
2.2. Polarization Decomposition and Feature Extraction
2.3. Feature Compression
2.3.1. Auto-Encoder
2.3.2. Sparse Auto-Encoder with Non-Negativity Constraint
2.4. The Crop Discrimination Network with Multi-Scale Features (MSCDN)
3. Experiments and Result Analysis
3.1. PolSAR Data
3.2. Evaluation Criteria
3.3. Results and Analysis
3.3.1. Comparison of the Dimensionality Reduction Methods
3.3.2. Comparison of the Classifier with Different Classification Methods
3.3.3. The Performance for the Different Size of Input Sample
3.3.4. Comparison of Overall Processing Procedures
4. Discussion
4.1. The Effect of NC-SAE
4.2. The Effect of MSCDN Classifier
4.3. Future Work
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Feature Extraction Schemes | Features | Dimension |
---|---|---|
Features based on measured data | Polarization intensities , , | 3 |
Amplitude of HH-VV correlation | 1 | |
Phase difference of HH-VV | 1 | |
Co-polarized ratio | 1 | |
Cross-polarized ratio | 1 | |
Co-polarization ratio | 1 | |
Degrees of polarization | 2 | |
Incoherent decomposition | Freeman decomposition | 5 |
Yamaguchi decomposition | 7 | |
Cloude decomposition | 3 | |
Huynen decomposition | 9 | |
Other decomposition | Null angle parameters | 2 |
Sum | 36 |
Layer | Detailed Description | Output Size | |||
---|---|---|---|---|---|
Kernel Size | Number | Stride | Padding | ||
Input | -- | 35 × 35 × 9 | |||
Conv_1 | 5 × 5 × 9 | 64 | [2,2] | same | 18 × 18 × 64 |
Conv_2 | 3 × 3 × 64 | 128 | [1,1] | same | 18 × 18 × 128 |
Maxpool_1 | 2 × 2 × 128 | -- | [2,2] | [0,0,0,0] | 9 × 9 × 128 |
Conv_3 | 3 × 3 × 128 | 256 | [1,1] | same | 9 × 9 × 256 |
Conv_4 | 3 × 3 × 256 | 128 | [2,2] | same | 5 × 5 × 128 |
Conv_5 | 3 × 3 × 128 | 128 | [1,1] | same | 5 × 5 × 128 |
Conv_6 | 1 × 1 × 256 | 32 | [1,1] | same | 9 × 9 × 32 |
Conv_7 | 1 × 1 × 128 | 64 | [1,1] | same | 5 × 5 × 64 |
Maxpool_2 | 2 × 2 × 128 | -- | [2,2] | [0,0,0,0] | 2 × 2 × 128 |
Fc_1,2,3 | -- | 256 | -- | -- | 1 × 1 × 256 |
Fc_4 | -- | M | -- | -- | 1 × 1 × M |
Softmax | softmax | 1 × 1 × M |
Crop Type | Crop Code | Number of Pixels | Total Crop Area (%) |
---|---|---|---|
Unknown | Unk | 1,323,612 | 13,236 ha (39.12%) |
Lentil | Len | 217,186 | 2172 ha (6.42%) |
Durum Wheat | Duw | 101,299 | 1013 ha (2.99%) |
Spring Wheat | Spw | 577,109 | 5771 ha (17.05%) |
Field Pea | Fip | 255,108 | 2551 ha (7.54%) |
Oat | Oat | 70,643 | 706 ha (2.09%) |
Canola | Can | 459,096 | 4591 ha (13.57%) |
Grass | Gra | 23,452 | 235 ha (0.69%) |
Mixed Pasture | Mip | 15,799 | 158 ha (0.47%) |
Mixed Hay | Mih | 28,756 | 288 ha (0.85%) |
Barley | Bar | 108,133 | 1081 ha (3.20%) |
Summer fallow | Suf | 22,445 | 224 ha (0.66%) |
Flax | Fla | 131,296 | 1313 ha (3.88%) |
Canary seed | Cas | 47,202 | 472 ha (1.39%) |
Chemical fallow | Chf | 2682 | 27 ha (0.08%) |
Total | 3,383,818 | 33,838 ha (100%) |
Method | Classification Performance | |||
---|---|---|---|---|
VA (%) | OA (%) | Kappa (%) | CPU Time | |
PCA | 80.63 | 87.92 | 85.23 | 0.4521 s |
LLE | 81.18 | 88.03 | 85.41 | 16.8427 s |
S-SAE | 91.29 | 94.24 | 93.03 | 5.5383 s |
NC-SAE | 90.26 | 94.23 | 93.05 | 0.5193 s |
Method | Recall Rates for 14 Types of Crops and OA (%) | ||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Len | Duw | Spw | Fip | Oat | Can | Gra | Mip | Mih | Bar | Suf | Fla | Cas | Chf | OA | |
SVM | 91.4 | 1.8 | 98.3 | 93.9 | 2.3 | 95.3 | 69.5 | 8.5 | 42.1 | 3.0 | 45.4 | 40.8 | 5.8 | 0 | 75.22 |
CNN | 97.8 | 69.2 | 96.5 | 99.7 | 80.5 | 99.8 | 89.4 | 69.0 | 76.3 | 84.6 | 92.2 | 93.3 | 90.2 | 42.9 | 94.23 |
MSCDN | 99.6 | 98.4 | 99.6 | 99.8 | 97.9 | 99.9 | 98.6 | 97.3 | 96.4 | 98.1 | 98.9 | 98.5 | 99.4 | 89.9 | 99.33 |
Ground Truth | |||||||||
---|---|---|---|---|---|---|---|---|---|
CNN | MSCDN | ||||||||
Duw | Mip | Mih | Chf | Duw | Mip | Mih | Chf | ||
Classified Image | Len | 0.58 | 1.93 | 0.79 | 55.9 | 0.09 | 0.04 | 0.26 | 8.98 |
Duw | 69.2 | 1.80 | 0.01 | 0 | 98.4 | 0.65 | 0 | 0 | |
Spw | 22.9 | 1.31 | 1.75 | 0 | 0.78 | 0.14 | 0.38 | 0 | |
Fip | 0.25 | 0.07 | 0.13 | 0.33 | 0.18 | 0.04 | 0.08 | 0.67 | |
Oat | 1.18 | 0.02 | 0.36 | 0 | 0 | 0.61 | 0.01 | 0 | |
Can | 0.59 | 0 | 0.53 | 0 | 0.22 | 0 | 0.46 | 0 | |
Gra | 0 | 9.03 | 11.7 | 0.07 | 0 | 0 | 2.10 | 0.26 | |
Mip | 0.01 | 69.0 | 4.84 | 0 | 0.01 | 97.3 | 0.05 | 0 | |
Mih | 0.07 | 3.25 | 76.3 | 0 | 0 | 0.73 | 96.3 | 0 | |
Bar | 3.98 | 0.06 | 0.21 | 0 | 0.21 | 0.07 | 0 | 0 | |
Suf | 0 | 9.54 | 0 | 0 | 0.04 | 0 | 0.23 | 0 | |
Fla | 0.92 | 3.99 | 3.30 | 0.70 | 0.01 | 0.33 | 0.01 | 0.11 | |
Cas | 0.21 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
Chf | 0 | 0 | 0 | 42.9 | 0 | 0 | 0 | 89.9 |
Input Sample Size | Classification Accuracy (%) | ||
---|---|---|---|
VA | OA | Kappa | |
15 × 15 | 91.07 | 95.21 | 94.21 |
35 × 35 | 98.56 | 99.33 | 99.19 |
55 × 55 | 99.10 | 99.47 | 99.37 |
Input Sample Size | Classification Accuracy (%) | ||
---|---|---|---|
VA | OA | Kappa | |
15 × 15 | 90.26 | 94.23 | 93.05 |
35 × 35 | 96.53 | 97.91 | 97.48 |
55 × 55 | 97.07 | 98.29 | 97.94 |
Method | Input Sample Size | Classification Accuracy (%) | ||
---|---|---|---|---|
VA | OA | Kappa | ||
LSTM | -- | 73.10 | 76.43 | 70.15 |
LLE + SVM | -- | -- | 65.51 | 55.48 |
S-SAE + SVM | -- | -- | 78.48 | 72.92 |
PCA + CNN | 15 × 15 | 80.63 | 87.92 | 85.23 |
35 × 35 | 93.55 | 96.10 | 95.30 | |
S-SAE + CNN | 15 × 15 | 91.29 | 94.24 | 93.03 |
35 × 35 | 96.81 | 98.25 | 97.90 | |
S-SAE + MSCDN | 15 × 15 | 92.05 | 96.11 | 95.32 |
35 × 35 | 98.12 | 99.06 | 98.87 | |
NC-SAE + CNN | 15 × 15 | 90.26 | 94.23 | 93.05 |
35 × 35 | 96.53 | 97.91 | 97.48 | |
NC-SAE + MSCDN | 15 × 15 | 91.07 | 95.21 | 94.21 |
35 × 35 | 98.56 | 99.33 | 99.19 |
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Zhang, W.-T.; Wang, M.; Guo, J.; Lou, S.-T. Crop Classification Using MSCDN Classifier and Sparse Auto-Encoders with Non-Negativity Constraints for Multi-Temporal, Quad-Pol SAR Data. Remote Sens. 2021, 13, 2749. https://doi.org/10.3390/rs13142749
Zhang W-T, Wang M, Guo J, Lou S-T. Crop Classification Using MSCDN Classifier and Sparse Auto-Encoders with Non-Negativity Constraints for Multi-Temporal, Quad-Pol SAR Data. Remote Sensing. 2021; 13(14):2749. https://doi.org/10.3390/rs13142749
Chicago/Turabian StyleZhang, Wei-Tao, Min Wang, Jiao Guo, and Shun-Tian Lou. 2021. "Crop Classification Using MSCDN Classifier and Sparse Auto-Encoders with Non-Negativity Constraints for Multi-Temporal, Quad-Pol SAR Data" Remote Sensing 13, no. 14: 2749. https://doi.org/10.3390/rs13142749