Hyperspectral Image Dimensionality Reduction Algorithm Based on Spatial–Spectral Adaptive Multiple Manifolds
Abstract
:1. Introduction
2. Related Work
2.1. Super-Pixel Segmentation Algorithm
2.2. Dimensionality Reduction Algorithm Based on Manifold Learning
3. Proposed Method
3.1. Regional Adaptive Weight Representation
3.2. Space–Spectrum Adaptive Multi-Manifold Analysis
4. Experimental Results and Analysis
4.1. Datasets
4.2. Implementation Details
4.3. Comparison with Existing Methods
4.3.1. Classification Results on the IP Dataset
4.3.2. Classification Results on the UP Dataset
4.4. Parameter Analysis
4.4.1. Inter-Manifold and Intra-Manifold near Neighbors a and b
4.4.2. Spatial Window Size p and the Number of Near Neighbors k
4.4.3. Balancing Spectral Information Parameters α and Spatial Information Parameters β
4.4.4. The Dimension of the Subspace d after Dimensionality Reduction
4.5. Ablation Experiments
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Category Number | Category Name | Sample Number | Color |
---|---|---|---|
1 | Alfalfa | 46 | |
2 | Corn-notill | 1428 | |
3 | Corn-mintill | 830 | |
4 | Corn | 237 | |
5 | Grass-pasture | 483 | |
6 | Grass-trees | 730 | |
7 | Grass-pasture-mowed | 28 | |
8 | Hay-windrowed | 478 | |
9 | Oats | 20 | |
10 | Soybean-notill | 972 | |
11 | Soybean-mintill | 2455 | |
12 | Soybean-clean | 593 | |
13 | Wheat | 205 | |
14 | Woods | 1265 | |
15 | Buildings-grass-trees-drives | 386 | |
16 | Stone-steel-towers | 93 | |
Total | 10,249 |
Category Number | Category Name | Sample Number | Color |
---|---|---|---|
1 | Asphalt | 6631 | |
2 | Meadows | 18,649 | |
3 | Gravel | 2099 | |
4 | Trees | 3064 | |
5 | Painted metal sheets | 1345 | |
6 | Bare Soil | 5029 | |
7 | Bitumen | 1330 | |
8 | Self-blocking bricks | 3682 | |
9 | Shadows | 947 | |
Total | 42,776 |
No. | PCA | LPP | MFA | NSPE | MLE | MMDA | SSMRPE | SPCA | SSAMMA |
---|---|---|---|---|---|---|---|---|---|
C1 | 73.67 | 84.97 | 86.38 | 72.07 | 68.85 | 85.08 | 81.23 | 98.87 | 96.23 |
C2 | 92.56 | 79.96 | 89.29 | 66.85 | 72.06 | 80.74 | 80.58 | 90.54 | 90.87 |
C3 | 82.05 | 58.99 | 85.60 | 63.20 | 70.67 | 65.03 | 74.09 | 91.27 | 93.96 |
C4 | 60.39 | 32.02 | 58.92 | 67.28 | 48.82 | 68.25 | 71.20 | 93.82 | 92.38 |
C5 | 95.43 | 94.07 | 95.47 | 72.81 | 79.58 | 93.28 | 93.28 | 97.28 | 96.97 |
C6 | 97.22 | 97.23 | 97.56 | 76.58 | 89.25 | 98.53 | 97.56 | 97.01 | 99.78 |
C7 | 80.11 | 45.06 | 84.68 | 66.50 | 68.91 | 84.31 | 91.65 | 91.52 | 95.86 |
C8 | 96.58 | 97.89 | 98.78 | 79.21 | 93.63 | 98.60 | 97.28 | 98.07 | 99.69 |
C9 | 85.70 | 68.00 | 87.82 | 66.00 | 57.98 | 68.87 | 69.87 | 98.64 | 94.22 |
C10 | 73.35 | 62.25 | 75.65 | 67.25 | 68.46 | 73.38 | 78.99 | 76.49 | 78.87 |
C11 | 85.68 | 85.62 | 84.87 | 88.16 | 75.72 | 83.64 | 85.31 | 90.72 | 92.34 |
C12 | 68.97 | 71.06 | 68.36 | 71.02 | 62.39 | 81.25 | 75.82 | 85.25 | 89.70 |
C13 | 95.02 | 99.30 | 95.13 | 80.03 | 88.24 | 99.01 | 98.86 | 99.52 | 100 |
C14 | 93.65 | 97.61 | 94.65 | 95.16 | 83.18 | 97.12 | 97.58 | 97.34 | 98.53 |
C15 | 65.22 | 62.35 | 68.30 | 57.55 | 50.03 | 72.45 | 97.36 | 90.65 | 88.38 |
C16 | 95.83 | 86.51 | 96.56 | 76.85 | 71.34 | 88.24 | 68.45 | 91.36 | 92.66 |
OA | 82.65 | 83.02 | 86.25 | 72.35 | 78.23 | 85.86 | 88.75 | 91.34 | 91.59 |
AA | 80.28 | 67.33 | 83.06 | 70.47 | 75.62 | 83.06 | 86.37 | 92.60 | 92.76 |
K | 81.26 | 76.87 | 84.87 | 71.56 | 76.44 | 84.57 | 87.60 | 90.67 | 90.48 |
T(s) | 0.17 | 4.26 | 7.93 | 8.57 | 20.15 | 30.54 | 42.82 | 0.48 | 46.52 |
No. | PCA | LPP | MFA | NSPE | MLE | MMDA | SSMRPE | SPCA | SSAMMA |
---|---|---|---|---|---|---|---|---|---|
C1 | 83.20 | 85.86 | 87.38 | 85.96 | 82.09 | 92.45 | 92.64 | 79.64 | 95.61 |
C2 | 90.05 | 91.50 | 95.26 | 91.63 | 89.47 | 94.65 | 97.89 | 93.08 | 97.85 |
C3 | 63.68 | 64.22 | 75.03 | 56.06 | 60.56 | 68.73 | 76.21 | 97.54 | 92.03 |
C4 | 78.14 | 81.03 | 82.17 | 79.90 | 72.38 | 90.76 | 81.23 | 85.33 | 88.38 |
C5 | 97.38 | 97.48 | 98.25 | 92.74 | 95.65 | 88.23 | 99.58 | 96.88 | 99.85 |
C6 | 86.56 | 62.47 | 67.28 | 59.42 | 79.26 | 79.15 | 95.66 | 94.80 | 97.21 |
C7 | 81.87 | 83.88 | 88.24 | 65.50 | 75.60 | 73.24 | 87.45 | 92.38 | 98.43 |
C8 | 75.12 | 78.86 | 78.81 | 67.24 | 71.83 | 85.43 | 81.18 | 92.55 | 89.07 |
C9 | 98.50 | 98.51 | 98.52 | 69.88 | 94.57 | 95.32 | 99.76 | 95.53 | 99.38 |
OA | 85.93 | 86.04 | 87.38 | 74.26 | 79.82 | 87.58 | 92.35 | 90.96 | 96.53 |
AA | 82.65 | 85.35 | 86.50 | 70.45 | 76.67 | 86.83 | 91.19 | 91.97 | 94.12 |
K | 82.76 | 82.76 | 83.57 | 72.12 | 73.78 | 86.72 | 91.32 | 88.16 | 95.38 |
T(s) | 0.68 | 96.22 | 9.64 | 106.54 | 24.62 | 41.28 | 44.64 | 0.51 | 10.71 |
Dataset | Model | Module | Metrics | |||
---|---|---|---|---|---|---|
SAWRM | SSMMM | OA | AA | K | ||
IP | M1 | × | × | 82.65 | 80.72 | 79.68 |
M2 | × | 88.04 | 87.13 | 86.19 | ||
M3 | × | 88.27 | 87.36 | 86.50 | ||
M4 | 90.56 | 89.71 | 88.02 | |||
PU | M1 | × | × | 92.87 | 91.48 | 90.16 |
M2 | × | 95.11 | 94.00 | 93.14 | ||
M3 | × | 95.34 | 94.29 | 94.05 | ||
M4 | 97.86 | 97.06 | 96.22 |
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Xu, S.; Geng, S.; Yang, Q.; Gao, H. Hyperspectral Image Dimensionality Reduction Algorithm Based on Spatial–Spectral Adaptive Multiple Manifolds. Appl. Sci. 2023, 13, 9180. https://doi.org/10.3390/app13169180
Xu S, Geng S, Yang Q, Gao H. Hyperspectral Image Dimensionality Reduction Algorithm Based on Spatial–Spectral Adaptive Multiple Manifolds. Applied Sciences. 2023; 13(16):9180. https://doi.org/10.3390/app13169180
Chicago/Turabian StyleXu, Shufang, Sijie Geng, Qi Yang, and Hongmin Gao. 2023. "Hyperspectral Image Dimensionality Reduction Algorithm Based on Spatial–Spectral Adaptive Multiple Manifolds" Applied Sciences 13, no. 16: 9180. https://doi.org/10.3390/app13169180
APA StyleXu, S., Geng, S., Yang, Q., & Gao, H. (2023). Hyperspectral Image Dimensionality Reduction Algorithm Based on Spatial–Spectral Adaptive Multiple Manifolds. Applied Sciences, 13(16), 9180. https://doi.org/10.3390/app13169180