Toward Real Hyperspectral Image Stripe Removal via Direction Constraint Hierarchical Feature Cascade Networks
Abstract
:1. Introduction
1.1. Model-Driven Destriping Method
1.2. Data-Driven Destriping Method
1.3. Contributes
- The training stripe samples for TRS-DCHC are both realistic and simulated data. On one hand, the model can process stripes with unknown distribution and structure. On the other hand, there is no need to thoroughly design the complicated stripe generation method; moreover, a blind destriping dataset can be obtained.
- In the stripe sample extraction and generation part, we mainly focus on spatial context information by adopting the strategy of extrapolating from local to global, and by proposing a constraint on the direction extraction strategy of points and lines, and lines and surfaces that extracts stripe samples from a global perspective.
- In the stripe removal part, we propose a multi-scale dense hierarchical feature cascading wavelet network through multi-scale feature extraction and multilevel feature fusion to obtain abundant information flow for the stripe component. In particular, we use the discrete wavelet transform (DWT) to explicitly decompose the input data into different frequency information as network input. This strategy combines the prior knowledge of an image with deep learning, which is more suitable for stripe removal. Moreover, it can alleviate the challenge of training, reduce information loss, and maintain spectrum consistency.
2. Materials and Methods
2.1. HSI Degradation Formula
2.2. Direction Constrained Stripe Adaptive Unsupervised Extraction Subnetwork
2.3. Wavelet-Based Hierarchical Feature Cascaded Destriping Subnetwork
2.3.1. Wavelet Decomposition
2.3.2. Multi-Scale Hierarchical Feature Fusion
3. Experimental Results and Discussion
3.1. Experimental Datasets and Parameter Setting
3.2. Simulated Data Experimental Results and Analysis
3.3. Real Data Experimental Results and Analysis
3.3.1. GF-5 HSI Dataset
3.3.2. Zhuhai-1 Dataset
3.3.3. Hyperion EO-1 Dataset
3.3.4. HYDICE Urban Data Set
4. Discussion
4.1. Effectiveness toward Real Data Training Strategy
4.2. Effectiveness of DWT
4.3. Analysis of Direction Constrained Stripe Adaptive Unsupervised Extraction Subnetwork
4.4. The Time Costs
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
HSI | hyperspectral imaging |
CNNs | convolutional neural networks |
TRS-DCHC | toward real HSI stripe removal via direction constraint hierarchical feature cascade network |
PCA | principal component analysis |
DWT | discrete wavelet transform |
DCSC | direction constraint spatial context module |
SCARB | spatial context aggregation residual block |
MDL2G | multi-direction awarded local to global module |
IRNN | image spatial recurrent neural network |
TV | total variation |
LL | approximation of DWT image |
LH | horizontal detail |
HL | vertical detail |
HH | diagonal detail |
MSE | mean square error |
GF-5 | GaoFen-5 |
AHSI | advanced hyperspectral imager |
VNIR | visible and near-infrared |
SWIR | shortwave infrared |
CHEOS | China High-Resolution Earth Observation System |
WDC | Washington DC Mall HSI dataset |
ZhuHai-1 | Chinese ZhuHai-1 hyperspectral satellite |
EO-1 | Earth Observing-1 |
ASSTV | anisotropic spectral-spatial total variation |
LRTV | total-variation-regularized low-rank matrix factorization |
NGMeet | non-local meets global |
LRMR | low-rank matrix recovery |
TDL | tensor dictionary learning |
HSI-DeNet | hyperspectral image restoration via convolutional neural |
SGIDN | Satellite-ground integrated destriping network |
PSNR | peak signal-to-noise ratio |
SSIM | structural similarity |
ICV | inverse coefficient of variation |
MRD | mean relative deviation |
DCHC | the TRS-DCHC without incorporated real data |
TRS-DCHC-noW | the TRS-DCHC without DWT |
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Dataset | |||||
---|---|---|---|---|---|
WDC | GF-5 | ZhuHai-1 | EO-1 | Urban | |
Size | 1208 × 307 × 191 | 2008 × 2083 × 150/2008 × 2083 × 180 | 512 × 512 × 32 | 200 × 200 × 210 | 307 × 307 × 210 |
Sensor | Hydice | AHSI | CMOS | Hyperion | Hydice |
Case 1 | Case 2 | Case 3 | ||||
---|---|---|---|---|---|---|
MPSNR | MSSIM | MPSNR | MSSIM | MPSNR | MSSIM | |
LRTV [50] | 30.21 | 0.8206 | 29.26 | 0.7661 | 25.37 | 0.6427 |
LRMR [52] | 27.27 | 0.7102 | 28.91 | 0.7850 | 26.54 | 0.6771 |
TDL [53] | 27.51 | 0.7099 | 27.81 | 0.7364 | 26.16 | 0.6555 |
NGMeet [51] | 28.06 | 0.7434 | 33.64 | 0.8565 | 33.76 | 0.8703 |
ASSTV [49] | 29.36 | 0.7542 | 32.63 | 0.8423 | 33.61 | 0.8626 |
HSI-DeNet [31] | 36.17 | 0.9128 | 34.69 | 0.8974 | 33.84 | 0.8741 |
SGIDN [24] | 35.97 | 0.9036 | 35.85 | 0.9002 | 34.33 | 0.8846 |
DCHC | 42.83 | 0.9786 | 36.07 | 0.9013 | 36.86 | 0.9025 |
TRS-DCHC-noW | 42.21 | 0.9675 | 35.63 | 0.8942 | 36.07 | 0.8870 |
TRS-DCHC | 43.04 | 0.9867 | 36.22 | 0.9170 | 36.91 | 0.9078 |
Images | Index | Methods | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
LRTV | LRMR | TDL | NGMeet | ASSTV | HSI-DeNet | SGIDN | DCHC | TRS-DCHC-noW | TRS-DCHC | ||
GF-5 Scene 1 | MRD | 1.763 | 1.787 | 2.490 | 3.731 | 1.706 | 3.309 | 1.605 | 3.830 | 3.508 | 3.599 |
ICV | 8.031 | 8.685 | 8.428 | 6.915 | 7.924 | 5.906 | 7.767 | 12.141 | 11.505 | 13.341 | |
GF-5 Scene 2 | MRD | 0.778 | 0.555 | 3.218 | 3.838 | 1.002 | 1.101 | 1.506 | 0.539 | 0.630 | 0.522 |
ICV | 2.590 | 2.772 | 2.912 | 3.334 | 2.333 | 2.226 | 1.945 | 3.326 | 3.491 | 3.863 | |
GF-5 Scene 3 | MRD | 4.368 | 2.904 | 2.929 | 2.523 | 2.196 | 5.129 | 7.428 | 4.020 | 3.334 | 3.623 |
ICV | 5.393 | 3.723 | 3.747 | 4.062 | 2.863 | 3.441 | 6.493 | 2.408 | 3.590 | 3.317 | |
Zhuhai-1 Scene 1 | MRD | 1.804 | 2.176 | 1.961 | 1.539 | 1.382 | 1.415 | 2.697 | 2.052 | 1.126 | 0.960 |
ICV | 5.335 | 4.996 | 4.525 | 4.872 | 5.170 | 4.434 | 4.012 | 5.120 | 5.377 | 5.443 | |
Zhuhai-1 Scene 2 | MRD | 2.813 | 1.680 | 1.308 | 1.672 | 1.539 | 0.820 | 4.384 | 1.374 | 1.283 | 1.134 |
ICV | 3.607 | 3.913 | 4.260 | 4.765 | 4.475 | 4.368 | 5.261 | 4.310 | 4.235 | 5.484 | |
Hyperion EO-1 | MRD | 2.160 | 2.663 | 2.788 | 2.383 | 2.738 | 3.185 | 2.929 | 2.532 | 2.193 | 2.375 |
ICV | 2.507 | 1.854 | 2.102 | 2.044 | 1.978 | 2.606 | 1.953 | 2.672 | 2.598 | 2.714 | |
HYDICE Urban | MRD | 2.209 | 2.292 | 3.011 | 3.177 | 1.878 | 1.936 | 1.986 | 2.044 | 1.895 | 1.779 |
ICV | 0.785 | 0.779 | 0.8992 | 0.729 | 0.754 | 1.118 | 0.688 | 0.795 | 1.051 | 1.387 |
Methods | ||||||||
---|---|---|---|---|---|---|---|---|
LRTV | LRMR | TDL | NGMeet | ASSTV | HSI-DeNet | SGIDN | TRS-DCHC | |
Time costs (s) | 35.7 | 74.4 | 21.3 | 46.7 | 23.6 | 4.6 | 8.4 | 8.2 |
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Wang, C.; Xu, M.; Jiang, Y.; Zhang, G.; Cui, H.; Deng, G.; Lu, Z. Toward Real Hyperspectral Image Stripe Removal via Direction Constraint Hierarchical Feature Cascade Networks. Remote Sens. 2022, 14, 467. https://doi.org/10.3390/rs14030467
Wang C, Xu M, Jiang Y, Zhang G, Cui H, Deng G, Lu Z. Toward Real Hyperspectral Image Stripe Removal via Direction Constraint Hierarchical Feature Cascade Networks. Remote Sensing. 2022; 14(3):467. https://doi.org/10.3390/rs14030467
Chicago/Turabian StyleWang, Chengjun, Miaozhong Xu, Yonghua Jiang, Guo Zhang, Hao Cui, Guohui Deng, and Zhongyuan Lu. 2022. "Toward Real Hyperspectral Image Stripe Removal via Direction Constraint Hierarchical Feature Cascade Networks" Remote Sensing 14, no. 3: 467. https://doi.org/10.3390/rs14030467