Multi-View Structural Feature Extraction for Hyperspectral Image Classification
Abstract
:1. Introduction
2. Method
2.1. Dimension Reduction
2.2. Multi-View Feature Generation
2.3. Feature Fusion
Algorithm 1 Multi-view structural feature extraction |
Input: Input hyperspectral image I; Output: Hyperspectral image feature
|
3. Experiments
3.1. Experimental Setup
3.2. Classification Results
3.2.1. Indian Pines Dataset
3.2.2. Salinas Dataset
3.2.3. Honghu Dataset
4. Discussion
4.1. The Influence of Different Parameters
4.2. The Influence of Three Different Views
4.3. Effect of Different Hyperspectral Feature Methods
4.4. Computing Time
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
MNF | Minimum noise fraction |
HSI | Hyperspectral image |
PCA | Principal component analysis |
ICA | Independent component analysis |
APs | Attribute profiles |
EMAP | Extended morphological attribute profiles |
CNN | Convolutional neural network |
ERS | Entropy rate superpixel |
KPCA | Kernel PCA |
SVM | Support vector machine |
AVIRIS | Airborne Visible Infrared Imaging Spectrometer |
CA | Class accuracy |
OA | Overall accuracy |
AA | Average accuracy |
IFRF | Image fusion and recursive filtering |
SCMK | Superpixel-based classification via multiple kernels |
MSTV | Multi-scale total variation |
PCAEPFs | PCA-based edge-preserving features |
GTR | Generalized tensor regression |
EMP | Extended morphological profiles |
Gabor | Gabor filtering |
IID | Intrinsic image decomposition |
IAPs | Invariant attribute profiles |
LRR | Low rank representation |
RPNet | Random patches network |
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No. | Indian Pines Dataset | Salinas Dataset | Honghu Dataset | ||||||
---|---|---|---|---|---|---|---|---|---|
Name | Train | Test | Name | Train | Test | Name | Train | Test | |
1 | Alfalfa | 6 | 40 | Weeds_1 | 5 | 2004 | Red roof | 25 | 14,016 |
2 | Corn_N | 7 | 1421 | Weeds_2 | 5 | 3721 | Road | 25 | 3487 |
3 | Corn_M | 6 | 824 | Fallow | 5 | 1971 | Bare soil | 25 | 21,796 |
4 | Corn | 6 | 231 | Fallow_P | 5 | 1389 | Cotton | 25 | 163,260 |
5 | Grass_M | 6 | 477 | Fallow_S | 5 | 2673 | Cotton firewood | 25 | 6193 |
6 | Grass_T | 6 | 724 | Stubble | 5 | 3954 | Rape | 25 | 44,532 |
7 | Grass_P | 6 | 22 | Celery | 5 | 3574 | Chinese cabbage | 25 | 24,078 |
8 | Hay_W | 7 | 471 | Grapes | 5 | 11,266 | Packchoi | 25 | 4029 |
9 | Oats | 6 | 14 | Soil | 5 | 6198 | Cabbage | 25 | 10,794 |
10 | Soybean_N | 7 | 965 | Corn | 5 | 3273 | Tuber mustard | 25 | 12,369 |
11 | Soybean_M | 8 | 2447 | Lettuce_4 | 5 | 1063 | Brassica parachinensis | 25 | 10,990 |
12 | Soybean_C | 6 | 587 | Lettuce_5 | 5 | 1922 | Brassica chinensis | 25 | 8929 |
13 | Wheat | 6 | 199 | Lettuce_6 | 5 | 911 | Small Brassica chinensis | 25 | 22,482 |
14 | Woods | 6 | 1259 | Lettuce_7 | 5 | 1065 | Lactuca sativa | 25 | 7331 |
15 | Buildings | 6 | 380 | Vinyard_U | 5 | 7263 | Celtuce | 25 | 977 |
16 | Stone | 7 | 86 | Vinyard_T | 5 | 1802 | Film covered | 25 | 7237 |
17 | Total | 102 | 10,147 | Total | 80 | 54,049 | Romaine lettuce | 25 | 2985 |
18 | Carrot | 25 | 3192 | ||||||
19 | White radish | 25 | 8687 | ||||||
20 | Garlic sprout | 25 | 3461 | ||||||
21 | Broad bean | 25 | 1303 | ||||||
22 | Tree | 25 | 4015 | ||||||
Total | 550 | 386,143 |
Class | SVM | IFRF | EMAP | SCMK | MSTV | PCAEPFs | GTR | Our Method |
---|---|---|---|---|---|---|---|---|
1 | 31.53 (9.74) | 63.03 (27.93) | 94.21 (9.19) | 98.00 (1.05) | 95.92 (10.41) | 98.02 (4.65) | 96.75 (2.06) | 100.0 (0.00) |
2 | 47.00 (6.00) | 70.64 (15.98) | 59.74 (8.25) | 60.27 (11.34) | 86.93 (5.35) | 76.38 (7.21) | 55.64 (8.15) | 86.37 (5.87) |
3 | 34.03 (14.66) | 48.62 (12.35) | 53.23 (13.54) | 57.23 (13.07) | 71.01 (8.26) | 73.79 (13.56) | 47.42 (8.58) | 83.07 (16.27) |
4 | 26.70 (6.66) | 52.45 (11.34) | 36.54 (6.17) | 94.42 (4.94) | 67.17 (9.51) | 66.83 (7.53) | 72.47 (11.74) | 86.05 (10.51) |
5 | 63.24 (9.75) | 75.81 (10.59) | 67.96 (10.27) | 78.97 (12.76) | 97.49 (3.45) | 93.86 (6.29) | 83.06 (9.97) | 98.30 (3.94) |
6 | 79.83 (8.04) | 91.26 (4.32) | 90.31 (3.84) | 89.78 (11.19) | 98.70 (2.05) | 94.28 (3.29) | 88.07 (5.81) | 99.96 (0.13) |
7 | 31.35 (14.53) | 57.65 (21.85) | 61.05 (20.27) | 97.27 (2.35) | 98.70 (2.10) | 73.05 (31.40) | 99.09 (2.87) | 80.73 (25.62) |
8 | 95.57 (2.12) | 99.89 (0.27) | 100.0 (0.00) | 100.0 (0.00) | 100.0 (0.00) | 100.0 (0.00) | 88.77 (7.52) | 100.0 (0.00) |
9 | 17.18 (8.59) | 28.91 (13.60) | 40.28 (10.36) | 100.0 (0.00) | 96.67 (10.54) | 72.87 (21.18) | 98.57 (4.52) | 98.08 (4.27) |
10 | 41.51 (5.35) | 66.42 (8.13) | 52.5 (10.53) | 67.95 (13.90) | 81.22 (9.40) | 78.55 (10.42) | 54.30 (8.60) | 90.70 (8.08) |
11 | 60.65 (8.31) | 73.91 (6.22) | 75.15 (9.85) | 59.69 (9.88) | 92.28 (4.04) | 90.14 (4.52) | 41.52 (11.20) | 89.40 (6.90) |
12 | 29.10 (5.43) | 56.65 (9.17) | 48.07 (5.31) | 65.54 (9.57) | 78.81 (12.25) | 77.19 (12.22) | 67.00 (8.52) | 83.81 (15.76) |
13 | 79.14 (3.12) | 74.79 (12.60) | 85.46 (9.48) | 100.0 (0.00) | 100.0 (0.00) | 97.45 (4.78) | 99.25 (1.09) | 100.0 (0.00) |
14 | 88.92 (5.36) | 93.03 (4.09) | 91.79 (4.98) | 81.92 (9.64) | 99.54 (0.65) | 99.66 (0.45) | 85.77 (9.36) | 99.61 (0.24) |
15 | 33.04 (7.36) | 59.07 (13.37) | 65.61 (14.81) | 76.47 (11.34) | 89.69 (10.56) | 93.72 (5.04) | 65.53 (11.80) | 93.78 (6.94) |
16 | 76.05 (19.59) | 93.16 (5.08) | 90.53 (7.63) | 97.79 (0.37) | 93.82 (3.82) | 98.37 (1.07) | 97.67 (4.17) | 98.80 (0.02) |
OA | 53.30 (3.23) | 70.09 (4.51) | 67.70 (5.14) | 71.20 (3.52) | 88.25 (2.45) | 85.58 (3.35) | 63.25 (3.46) | 90.32 (3.58) |
AA | 52.18 (2.78) | 69.08 (4.43) | 69.53 (3.88) | 82.83 (1.89) | 90.49 (1.93) | 86.51 (2.53) | 77.55 (1.61) | 93.04 (2.75) |
Kappa | 47.57 (3.44) | 66.35 (4.72) | 63.67 (5.52) | 67.50 (3.90) | 86.63 (2.77) | 83.65 (3.75) | 58.79 (3.59) | 88.96 (4.02) |
Classes | SVM | IFRF | EMAP | SCMK | MSTV | PCAEPFs | GTR | Our Method |
---|---|---|---|---|---|---|---|---|
1 | 99.13 (0.97) | 99.95 (0.16) | 99.98 (0.05) | 97.53 (5.50) | 100.0 (0.00) | 100.0 (0.00) | 97.33 (2.25) | 100.0 (0.00) |
2 | 98.74 (1.00) | 98.66 (1.31) | 99.75 (0.14) | 98.90 (3.48) | 99.97 (0.05) | 99.95 (0.08) | 99.84 (0.32) | 100.0 (0.00) |
3 | 79.50 (8.77) | 98.34 (1.93) | 94.55 (2.25) | 96.40 (7.65) | 97.87 (2.83) | 97.72 (2.38) | 85.58 (8.80) | 99.47 (0.10) |
4 | 96.00 (2.41) | 91.59 (3.95) | 95.93 (1.39) | 90.01 (8.71) | 96.90 (1.79) | 93.19 (4.02) | 99.83 (0.07) | 97.03 (0.28) |
5 | 94.01 (7.06) | 97.08 (1.77) | 96.66 (6.27) | 97.93 (1.56) | 96.86 (1.49) | 98.98 (3.21) | 90.12 (6.82) | 99.96 (0.01) |
6 | 99.79 (0.53) | 100.0 (0.00) | 99.41 (0.73) | 99.75 (0.00) | 98.34 (2.92) | 99.98 (0.04) | 98.89 (2.86) | 99.97 (0.01) |
7 | 95.27 (3.61) | 97.43 (2.27) | 96.27 (3.16) | 99.85 (0.06) | 96.23 (5.52) | 99.92 (0.06) | 99.18 (0.65) | 99.83 (0.01) |
8 | 64.94 (4.82) | 91.99 (3.74) | 80.00 (7.80) | 69.96 (8.23) | 92.73 (5.95) | 93.66 (6.53) | 69.49 (11.96) | 98.22 (2.96) |
9 | 98.66 (0.87) | 98.95 (0.67) | 98.99 (0.21) | 99.91 (0.19) | 98.61 (1.03) | 99.58 (0.46) | 98.84 (0.95) | 99.33 (0.67) |
10 | 77.21 (6.38) | 97.51 (1.62) | 87.98 (4.27) | 82.44 (14.88) | 95.02 (7.28) | 99.31 (0.97) | 80.51 (10.31) | 98.98 (0.19) |
11 | 82.43 (10.35) | 93.20 (3.51) | 79.41 (10.37) | 94.93 (5.93) | 99.65 (0.78) | 95.04 (4.63) | 96.43 (2.38) | 97.42 (7.25) |
12 | 89.11 (7.96) | 94.98 (5.23) | 88.64 (6.36) | 91.05 (12.43) | 99.11 (1.13) | 94.71 (4.32) | 98.12 (5.45) | 98.62 (2.48) |
13 | 79.04 (12.81) | 83.39 (11.25) | 92.32 (3.45) | 93.94 (10.28) | 95.32 (6.68) | 91.81 (11.56) | 98.79 (0.71) | 81.85 (6.29) |
14 | 85.09 (12.77) | 84.15 (17.99) | 97.23 (1.08) | 88.92 (3.95) | 89.54 (10.17) | 92.54 (10.36) | 91.01 (5.19) | 94.81 (6.19) |
15 | 46.93 (5.71) | 70.34 (13.57) | 56.84 (7.75) | 82.66 (11.52) | 84.53 (7.06) | 84.52 (8.47) | 65.16 (14.05) | 95.59 (4.72) |
16 | 93.27 (5.57) | 99.12 (0.76) | 96.43 (2.26) | 93.83 (10.51) | 97.68 (4.67) | 99.96 (0.08) | 86.38 (7.44) | 100.0 (0.00) |
OA | 80.08 (1.96) | 90.67 (3.33) | 85.54 (2.57) | 88.70 (2.84) | 94.46 (1.47) | 95.12 (1.37) | 85.59 (3.54) | 98.13 (0.59) |
AA | 86.19 (1.07) | 93.54 (1.84) | 91.27 (1.07) | 92.38 (2.16) | 96.14 (1.18) | 96.30 (0.94) | 90.96 (1.67) | 97.57 (0.75) |
Kappa | 77.87 (2.15) | 89.66 (3.65) | 83.97 (2.81) | 87.47 (3.15) | 93.84 (1.64) | 94.56 (1.53) | 83.97 (3.94) | 97.92 (0.67) |
Classes | SVM | IFRF | EMAP | SCMK | MSTV | PCAEPFs | GTR | Our Method |
---|---|---|---|---|---|---|---|---|
1 | 88.17 | 98.22 | 91.04 | 86.91 | 97.73 | 89.84 | 75.41 | 89.69 |
2 | 54.73 | 67.79 | 64.54 | 86.23 | 73.31 | 78.28 | 57.36 | 57.95 |
3 | 88.51 | 96.16 | 92.62 | 93.44 | 93.27 | 96.64 | 61.13 | 97.83 |
4 | 96.12 | 99.31 | 98.30 | 87.64 | 99.50 | 99.56 | 44.66 | 99.72 |
5 | 17.85 | 57.84 | 23.49 | 98.66 | 63.24 | 52.07 | 55.53 | 79.29 |
6 | 85.18 | 90.83 | 89.97 | 89.74 | 93.78 | 93.26 | 69.41 | 99.40 |
7 | 74.00 | 89.38 | 81.95 | 77.24 | 89.62 | 88.44 | 50.93 | 95.81 |
8 | 6.09 | 16.05 | 10.73 | 90.02 | 43.87 | 31.39 | 42.74 | 97.99 |
9 | 91.60 | 99.21 | 89.67 | 94.61 | 90.23 | 86.79 | 90.75 | 97.82 |
10 | 49.29 | 73.45 | 73.62 | 76.59 | 91.88 | 83.72 | 32.99 | 99.44 |
11 | 28.17 | 58.82 | 41.50 | 77.43 | 69.88 | 64.28 | 21.36 | 79.63 |
12 | 43.33 | 61.20 | 52.56 | 93.64 | 64.18 | 61.54 | 43.77 | 96.26 |
13 | 50.58 | 77.54 | 70.86 | 70.73 | 79.52 | 81.99 | 30.38 | 78.44 |
14 | 43.35 | 70.73 | 60.01 | 86.77 | 90.14 | 78.77 | 61.29 | 80.40 |
15 | 3.97 | 31.73 | 23.46 | 97.24 | 57.05 | 51.69 | 82.80 | 91.99 |
16 | 80.93 | 93.51 | 87.57 | 94.53 | 99.01 | 98.66 | 82.34 | 98.37 |
17 | 54.39 | 72.82 | 62.17 | 93.10 | 85.53 | 92.66 | 73.80 | 97.09 |
18 | 21.83 | 32.85 | 39.27 | 95.08 | 72.90 | 67.53 | 74.91 | 68.77 |
19 | 48.88 | 64.45 | 51.52 | 64.06 | 83.41 | 58.41 | 56.23 | 74.37 |
20 | 38.00 | 54.19 | 59.34 | 99.86 | 79.59 | 65.60 | 46.06 | 88.71 |
21 | 11.04 | 24.32 | 38.85 | 100.00 | 86.11 | 43.46 | 71.14 | 91.70 |
22 | 21.50 | 47.34 | 25.73 | 99.55 | 82.90 | 76.21 | 73.23 | 89.30 |
OA | 64.43 | 84.23 | 76.11 | 86.41 | 90.74 | 87.37 | 51.87 | 94.01 |
AA | 49.88 | 67.17 | 60.39 | 88.18 | 81.21 | 74.58 | 59.01 | 88.64 |
Kappa | 57.68 | 80.27 | 70.82 | 85.86 | 88.37 | 84.23 | 45.79 | 92.47 |
Local-View | Intra-View | Inter-View | OA | AA | Kappa | Time (s) |
---|---|---|---|---|---|---|
✓ | 87.22 | 85.08 | 85.51 | 3.68 | ||
✓ | 86.22 | 86.78 | 84.38 | 3.87 | ||
✓ | 87.35 | 90.66 | 85.63 | 3.95 | ||
✓ | ✓ | 88.32 | 89.41 | 86.71 | 4.17 | |
✓ | ✓ | 88.96 | 91.21 | 87.43 | 4.31 | |
✓ | ✓ | 88.25 | 92.07 | 86.58 | 4.48 | |
✓ | ✓ | ✓ | 90.32 | 93.04 | 88.96 | 5.36 |
Datasets | SVM | IFRF | EMAP | SCMK | MSTV | PCAEPFs | GTR | Our Method |
---|---|---|---|---|---|---|---|---|
Indian Pines | 5.53 | 2.32 | 3.25 | 4.49 | 4.35 | 2.67 | 2.04 | 5.36 |
Salinas | 21.08 | 2.68 | 4.56 | 3.23 | 12.34 | 12.98 | 2.49 | 19.35 |
Honghu | 128.26 | 16.55 | 69.94 | 15.75 | 45.67 | 20.79 | 9.37 | 102.23 |
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Liang, N.; Duan, P.; Xu, H.; Cui, L. Multi-View Structural Feature Extraction for Hyperspectral Image Classification. Remote Sens. 2022, 14, 1971. https://doi.org/10.3390/rs14091971
Liang N, Duan P, Xu H, Cui L. Multi-View Structural Feature Extraction for Hyperspectral Image Classification. Remote Sensing. 2022; 14(9):1971. https://doi.org/10.3390/rs14091971
Chicago/Turabian StyleLiang, Nannan, Puhong Duan, Haifeng Xu, and Lin Cui. 2022. "Multi-View Structural Feature Extraction for Hyperspectral Image Classification" Remote Sensing 14, no. 9: 1971. https://doi.org/10.3390/rs14091971