Land Cover Classification from Hyperspectral Images via Weighted Spatial-Spectral Kernel Collaborative Representation with Tikhonov Regularization
Abstract
:1. Introduction
- (1)
- A correlation coefficient-weighted spatial filtering operation is proposed to mine spatial-spectral features, which effectively reduces the spectral shift of the reconstructed central pixel.
- (2)
- By introducing a weighted spatial filtering operation into the KCRT and DKCRT methods, weighted spatial-spectral KCRT (WSSKCRT) and weighted spatial-spectral DKCRT (WSSDKCRT) methods, respectively, are proposed for land cover classification.
- (3)
- By optimizing parameters, the proposed method can effectively classify land cover types using hyperspectral images in the case of small-size labeled samples.
2. Materials and Methods
2.1. Data Collection
2.2. Classification Methods
2.2.1. Principle of the Original KCRT Method
2.2.2. Principle of the Original DKCRT Method
2.2.3. Principle of the Original KCRT-CK and JDKCRT Method
2.2.4. Principle of the Proposed WSSKCRT and WSSDKCRT Method
3. Results and Discussion
3.1. Hyperspectral Data Preprocessing
3.2. Parameter Optimization
3.3. Land Cover Classification
4. Conclusions
- (1)
- The proposed WSSKCRT method achieves the best classification result, in which OA, AA, and Kappa is 95.69%, 95.56%, and 0.9429, respectively.
- (2)
- WSSKCRT and WSSDKCRT outperform KCRT-CK and JDKCRT, respectively, which indicates that the proposed weighted spatial filtering operation can effectively alleviate the spectral shift caused by adjacency effect when mining the spatial-spectral features of hyperspectral images.
- (3)
- WSSKCRT and WSSDKCRT methods obtain the OA over 94% with only 540 labeled training samples, which indicates that the proposed methods can effectively classify land cover types under the situation of small-size labeled samples.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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No. | Class | Total Samples | Training Samples | Test Samples |
---|---|---|---|---|
1 | Asphalt | 6631 | 60 | 6571 |
2 | Meadows | 18,649 | 60 | 18,589 |
3 | Gravel | 2099 | 60 | 2039 |
4 | Trees | 3064 | 60 | 3004 |
5 | Painted metal sheets | 1345 | 60 | 1285 |
6 | Bare Soil | 5029 | 60 | 4969 |
7 | Bitumen | 1330 | 60 | 1270 |
8 | Self-Blocking Bricks | 3682 | 60 | 3622 |
9 | Shadows | 947 | 60 | 887 |
All classes | 42,776 | 540 | 42,236 |
Parameters | Methods | |||||
---|---|---|---|---|---|---|
DKCRT | KCRT | JDKCRT | KCRT-CK | WSSDKCRT | WSSKCRT | |
10−1 | 10−1 | 10−3 | 10−2 | 10−3 | 10−2 | |
10−3 | No application | 10−4 | No application | 10−4 | No application | |
T | No application | No application | 5 × 5 | 5 × 5 | 7 × 7 | 9 × 9 |
Class | DKCRT | KCRT | JDKCRT | KCRT-CK | WSSDKCRT | WSSKCRT |
---|---|---|---|---|---|---|
Asphalt | 74.47 | 71.71 | 92.34 | 92.22 | 91.54 | 91.56 |
Meadows | 81.45 | 80.59 | 95.41 | 95.22 | 96.70 | 97.51 |
Gravel | 85.85 | 77.78 | 94.77 | 90.32 | 95.70 | 91.48 |
Trees | 94.00 | 94.41 | 96.32 | 96.43 | 96.81 | 96.66 |
Painted metal sheets | 99.57 | 99.44 | 99.98 | 100.00 | 99.70 | 99.65 |
Bare Soil | 80.56 | 78.03 | 94.01 | 94.07 | 96.27 | 97.13 |
Bitumen | 92.61 | 90.86 | 98.38 | 96.83 | 99.35 | 97.50 |
Self-Blocking Bricks | 61.65 | 77.03 | 69.80 | 88.21 | 73.34 | 90.92 |
Shadows | 97.42 | 97.96 | 99.53 | 99.71 | 98.65 | 97.68 |
OA (%) | 80.89 | 80.70 | 92.92 | 94.16 | 94.02 | 95.69 |
AA (%) | 85.29 | 85.31 | 93.39 | 94.78 | 94.23 | 95.56 |
Kappa | 0.7535 | 0.7512 | 0.9064 | 0.9228 | 0.9208 | 0.9429 |
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Yang, R.; Fan, B.; Wei, R.; Wang, Y.; Zhou, Q. Land Cover Classification from Hyperspectral Images via Weighted Spatial-Spectral Kernel Collaborative Representation with Tikhonov Regularization. Land 2022, 11, 263. https://doi.org/10.3390/land11020263
Yang R, Fan B, Wei R, Wang Y, Zhou Q. Land Cover Classification from Hyperspectral Images via Weighted Spatial-Spectral Kernel Collaborative Representation with Tikhonov Regularization. Land. 2022; 11(2):263. https://doi.org/10.3390/land11020263
Chicago/Turabian StyleYang, Rongchao, Beilei Fan, Ren Wei, Yuting Wang, and Qingbo Zhou. 2022. "Land Cover Classification from Hyperspectral Images via Weighted Spatial-Spectral Kernel Collaborative Representation with Tikhonov Regularization" Land 11, no. 2: 263. https://doi.org/10.3390/land11020263