ICTH: Local-to-Global Spectral Reconstruction Network for Heterosource Hyperspectral Images
Abstract
:1. Introduction
- (1)
- We propose ICTH for heterogeneous hyperspectral image reconstruction, combining a CNN and a Transformer to achieve a coarse-to-fine reconstruction scheme, demonstrating excellent results on three hyperspectral datasets;
- (2)
- We propose an efficient plug-and-play spatial–spectral attention mechanism (S2AM) that simultaneously extracts fine-grained features in both spatial and spectral dimensions while maintaining a linear relationship between complexity and spatial dimensions;
- (3)
- We have refined the pre-processing operations on heterogeneous image data to enhance SR accuracy;
- (4)
- We present a vegetation-index-based assessment of the effectiveness of spectral reconstruction.
2. Related Work
2.1. Hyperspectral Image Reconstruction
2.2. Vision Transformer
3. Materials and Methods
3.1. Study Area and Experimental Design
3.2. Aerial Image Acquisition and Data Preprocessing
3.3. Remote Sensing Image Preprocessing
3.3.1. RGB and Multispectral Image Mosaic
3.3.2. Geometric Correction and Ortho-Stitching of Hyperspectral Data
3.4. Remote Sensing Image Alignment
3.5. Model Construction
3.5.1. Problem Formulation
3.5.2. Network Architecture
3.5.3. Spatial–Spectral Conv3D Block
3.5.4. Spatial–Spectral Attention Mechanism
3.5.5. High-Frequency Extractor
3.6. Model Performance Evaluation
3.6.1. The Visual Effects Evaluation Indicators
3.6.2. The Application of Evaluation Indicators
3.7. Training Setting
3.7.1. Dataset
3.7.2. Implementation Details
4. Results
4.1. Comparison with SOTA Methods
4.1.1. Quantitative Results
4.1.2. Qualitative Results
4.1.3. Integrated Assessment
4.2. Application Validation
4.2.1. Validation of the Application of VI
4.2.2. Verification of Generalizability
4.3. Ablation Study
4.3.1. Decomposition Ablation
4.3.2. Attention Comparison
5. Discussion
5.1. Importance of Geographic Alignment
5.2. Scalability Challenges in Spectral Reconstruction of UAV Hyperspectral Imagery
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Flight Setting Content | Parameters | Flight Setting Content | Parameters |
---|---|---|---|
Flight altitude | 50 m | Mainline angle | 182° |
Movement speed | 2.3 m/s | Head Pitch Angle | −90° |
Heading overlap rate | 94% | Distance between photos | F:3.7M / S:9.8M |
Bypass overlap rate | 89% | Photo interval | 2.0 SEC |
Model | NTIRE 2022 Dataset | Rice Field Dataset | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
MARE | RMSE | PSNR | SSIM | SAM | MARE | RMSE | PSNR | SSIM | SAM | |
HSCNN+ | 0.4048 | 0.0593 | 26.03 | 0.823 | 0.243 | 0.2280 | 0.0190 | 34.51 | 0.915 | 0.164 |
HRNET | 0.4178 | 0.0570 | 26.31 | 0.839 | 0.182 | 0.1880 | 0.0221 | 33.24 | 0.911 | 0.169 |
HINET | 0.3705 | 0.0503 | 27.80 | 0.871 | 0.140 | 0.1837 | 0.0193 | 34.46 | 0.910 | 0.180 |
EDSR | 0.3331 | 0.0456 | 27.99 | 0.879 | 0.201 | 0.1783 | 0.0199 | 34.19 | 0.919 | 0.164 |
HDNET | 0.2682 | 0.0373 | 29.87 | 0.915 | 0.128 | 0.1910 | 0.0192 | 34.46 | 0.915 | 0.169 |
AWAN | 0.2499 | 0.0367 | 31.22 | 0.916 | 0.101 | 0.2191 | 0.0224 | 33.14 | 0.908 | 0.171 |
MIRNET | 0.2012 | 0.0287 | 32.72 | 0.943 | 0.092 | 0.2016 | 0.0207 | 33.80 | 0.912 | 0.170 |
MPRNET | 0.2032 | 0.0284 | 32.87 | 0.946 | 0.102 | 0.1793 | 0.0201 | 34.06 | 0.918 | 0.162 |
Restormer | 0.1842 | 0.0280 | 33.22 | 0.945 | 0.094 | 0.1865 | 0.0188 | 34.86 | 0.920 | 0.162 |
MST++ | 0.1836 | 0.0279 | 33.41 | 0.951 | 0.085 | 0.1829 | 0.0192 | 34.42 | 0.918 | 0.162 |
Ours | 0.1672 | 0.0246 | 34.26 | 0.952 | 0.088 | 0.1772 | 0.0186 | 34.76 | 0.922 | 0.160 |
Model | MARE | RMSE | PSNR | SSIM | SAM |
---|---|---|---|---|---|
HSCNN+ | 0.4732 | 0.0200 | 34.02 | 0.872 | 0.264 |
HRNET | 0.3605 | 0.0167 | 35.64 | 0.893 | 0.246 |
HINET | 0.3783 | 0.0184 | 34.79 | 0.876 | 0.296 |
EDSR | 0.3895 | 0.0176 | 35.15 | 0.891 | 0.250 |
HDNET | 0.4103 | 0.0175 | 35.19 | 0.888 | 0.249 |
AWAN | 0.4688 | 0.0206 | 33.77 | 0.875 | 0.252 |
MIRNET | 0.4423 | 0.0186 | 34.64 | 0.874 | 0.263 |
MPRNET | 0.4444 | 0.0189 | 34.54 | 0.878 | 0.258 |
Restormer | 0.3740 | 0.0168 | 35.55 | 0.893 | 0.254 |
MST++ | 0.3912 | 0.0170 | 35.45 | 0.891 | 0.249 |
Ours | 0.3278 | 0.0162 | 35.90 | 0.900 | 0.249 |
CSSM | HFE | MSA | MARE | RMSE | PSNR | SSIM | SAM |
---|---|---|---|---|---|---|---|
0.2385 | 0.0211 | 33.62 | 0.891 | 0.200 | |||
✔ | W-MSA | 0.2018 | 0.0194 | 34.32 | 0.906 | 0.175 | |
✔ | 0.2180 | 0.0193 | 34.42 | 0.912 | 0.167 | ||
✔ | ✔ | W-MSA | 0.1914 | 0.0191 | 34.47 | 0.914 | 0.169 |
✔ | ✔ | S-MSA | 0.1859 | 0.0192 | 34.42 | 0.907 | 0.167 |
✔ | ✔ | SS2M | 0.1772 | 0.0186 | 34.76 | 0.922 | 0.160 |
Experimental Area | Alignment | MARE | RMSE | PSNR | SSIM | SAM |
---|---|---|---|---|---|---|
Area 1 | G | 0.1772 | 0.0186 | 34.76 | 0.922 | 0.160 |
w/o G | 0.3745 | 0.0463 | 26.73 | 0.787 | 0.306 | |
Area 2 | G | 0.3278 | 0.0162 | 35.90 | 0.900 | 0.248 |
w/o G | 11.9138 | 0.0602 | 24.66 | 0.645 | 0.434 |
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Zhou, H.; Liu, Z.; Huang, Z.; Wang, X.; Su, W.; Zhang, Y. ICTH: Local-to-Global Spectral Reconstruction Network for Heterosource Hyperspectral Images. Remote Sens. 2024, 16, 3377. https://doi.org/10.3390/rs16183377
Zhou H, Liu Z, Huang Z, Wang X, Su W, Zhang Y. ICTH: Local-to-Global Spectral Reconstruction Network for Heterosource Hyperspectral Images. Remote Sensing. 2024; 16(18):3377. https://doi.org/10.3390/rs16183377
Chicago/Turabian StyleZhou, Haozhe, Zhanhao Liu, Zhenpu Huang, Xuguang Wang, Wen Su, and Yanchao Zhang. 2024. "ICTH: Local-to-Global Spectral Reconstruction Network for Heterosource Hyperspectral Images" Remote Sensing 16, no. 18: 3377. https://doi.org/10.3390/rs16183377