Texture-Guided Multisensor Superresolution for Remotely Sensed Images
Abstract
:1. Introduction
- Versatile methodology: This paper proposes a versatile methodology for multisensor superresolution in remote sensing.
- Comprehensive evaluation: This paper demonstrates six different types of multisensor superresolution, which fuse the following image pairs: MS-PAN images (MS pan-sharpening), HS-PAN images (HS pan-sharpening), HS-MS images, optical-SAR images, long-wavelength infrared (LWIR) HS and RGB images, and DEM-MS images. The performance of TGMS is evaluated both quantitatively and qualitatively.
2. Texture-Guided Multisensor Superresolution
2.1. Data Transformation
2.2. Texture Descriptors
2.3. Multiscale Gradient Descent
2.4. Texture-Guided Filtering
3. Evaluation Methodology
3.1. Three Evaluation Scenarios
3.1.1. Synthetic Data Evaluation
3.1.2. Semi-Real Data Evaluation
3.1.3. Real Data Evaluation
3.2. Quality Indices
3.2.1. PSNR
3.2.2. SAM
3.2.3. ERGAS
3.2.4.
4. Experiments on Optical Data Fusion
4.1. Data Sets
4.1.1. MS Pan-Sharpening
- WorldView-3 Sydney: This data set was acquired by the visible and near-infrared (VNIR) and PAN sensors of WorldView-3 over Sydney, Australia, on 15 October 2014. (Available Online: https://www.digitalglobe.com/resources/imagery-product-samples/standard-satellite-imagery). The MS image has eight spectral bands in the VNIR range. The GSDs of the MS-PAN images are 1.6 m and 0.4 m, respectively. The study area is a 1000 × 1000 pixel size image at the resolution of the MS image, which includes parks and urban areas.
- WorldView-3 Fukushima: This data set was acquired by the VNIR and PAN sensors of WorldView-3 over Fukushima, Japan, on 10 August 2015. The MS image has eight spectral bands in the VNIR range. The GSDs of the MS-PAN images are 1.2 m and 0.3 m, respectively. The study area is a 1000×1000 pixel size image at the resolution of the MS image taken over a town named Futaba.
4.1.2. HS Pan-Sharpening
- ROSIS-3 University of Pavia: This data was acquired by the reflective optics spectrographic imaging system (ROSIS-3) optical airborne sensor over the University of Pavia, Italy, in 2003. A total of 103 bands covering the spectral range from 0.430 to 0.838 m are used in the experiment after removing 12 noisy bands. The study scene is a 560 × 320 pixel size image with a GSD of 1.3 m.
- Hyperspec-VNIR Chikusei: The airborne HS data set was taken by Headwall’s Hyperspec-VNIR-C imaging sensor over agricultural and urban areas in Chikusei, Ibaraki, Japan, on 19 July 2014. The data set comprises 128 bands in the spectral range from 0.363 to 1.018 m. The study scene is a 540 × 420 pixel size image with a GSD of 2.5 m. More detailed descriptions regarding the data acquisition and processing are given in [48].
4.1.3. HS-MS Data Fusion
- AVIRIS Indian Pines: This HS image was acquired by the AVIRIS sensor over the Indian Pines test site in northwestern Indiana, USA, in 1992 [49]. The AVIRIS sensor acquired 224 spectral bands in the wavelength range from 0.4 to 2.5 m with an FWHM of 10 nm. The image consists of 512 × 614 pixels at a GSD of 20 m. The study area is a 360 × 360 pixel size image with 192 bands after removing bands of strong water vapor absorption and low SNRs.
- AVIRIS Cuprite: This data set was acquired by the AVIRIS sensor over the Cuprite mining district in Nevada, USA, in 1995. (Available Online: http://aviris.jpl.nasa.gov/data/free_data.html). The entire data set comprises five reflectance images and this study used one of them saved in the file named f970619t01p02_r02_sc03.a.rfl. The full image consists of 512 × 614 pixels at a GSD of 20 m. The study area is a 420 × 360 pixel size image with 185 bands after removing noisy bands.
4.2. Results
4.2.1. MS Pan-Sharpening
4.2.2. HS Pan-Sharpening
4.2.3. HS-MS Fusion
4.2.4. Parameter Sensitivity Analysis
5. Experiments on Multimodal Data Fusion
5.1. Data Sets
- Optical-SAR fusion: This data set is composed of Landsat-8 and TerraSAR-X images taken over the Panama Canal, Panama. The Landsat-8 image was acquired on 5 March 2015. Bands 1–7 at a GSD of 30 m are used for the LR image of multisensor superresolution. The TerraSAR-X image was acquired with the sparing spotlight mode on 12 December 2013, and distributed as the enhanced ellipsoid corrected product at a pixel spacing of 0.24 m. (Available Online: http://www.intelligence-airbusds.com/en/23-sample-imagery). To reduce the speckle noise, the TerraSAR-X image was downsampled using a Gaussian filter for low-pass filtering so that the pixel spacing is equal to 3 m. The study area is a 1000 × 1000 pixel size image at the higher resolution. The backscattering coefficient is used for the experiment.
- LWIR-HS-RGB fusion: This data set comprises LWIR-HS and RGB images taken over an urban area near Thetford Mines in Québec, Canada, simultaneously on 21 May 2013. The data set was provided for the IEEE 2014 Geoscience and Remote Sensing Society (GRSS) Data Fusion Contest by Telops Inc. (Québec, QC, Canada) [50]. The LWIR-HS image was acquired by the Hyper-Cam, which is an airborne LWIR-HS imaging sensor based on a Fourier-transform spectrometer, with 84 bands covering the wavelengths from 7.8 to 11.5 m at a GSD of 1 m. The RGB image was acquired by a digital color camera at a GSD of 0.2 m. The study area is a 600 × 600 pixel size image at the higher resolution. There is a large degree of local misregistration (more than one pixel in the lower resolution) between the two images. The LWIR-HS image was registered to the RGB image by a projective transformation with manually selected control points.
- DEM-MS fusion: The DEM-MS data set was simulated using LiDAR-derived DEM and HS data taken over the University of Houston and its surrounding urban areas. The original data set was provided for the IEEE 2013 GRSS Data Fusion Contest [51]. The HS image has 144 spectral bands in the wavelength range from 0.4 to 1.0 m with an FWHM of 5 nm. Both images consist of 349 × 1905 pixels at a GSD of 2.5 m. The study area is a 344 × 500 pixel size image mainly over the campus of the University of Houston. To set a realistic problem, only four bands in the wavelengths of 0.46, 0.56, 0.66, and 0.82 m of the HS image are used as the HR-MS image. The DEM is degraded spatially using filtering and downsampling. Filtering was performed using an isotropic Gaussian PSF with an FWHM of the Gaussian function equal to the GSD ratio, which was set to four.
5.2. Results
6. Discussion
7. Conclusions and Future Lines
Acknowledgments
Conflicts of Interest
References
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Type of Fusion | Num. of Bands | Data Transform of HR Data | |
---|---|---|---|
LR | HR | ||
MS-PAN | Multiple | One | Histogram matching |
HS-PAN | Multiple | One | Histogram matching |
Optical-SAR | Multiple | One | Histogram matching |
HS-MS | Multiple | Multiple | Linear regression |
DEM-MS | One | Multiple | Local linear regression |
LWIR-HS-RGB | Multiple | Multiple | Linear regression |
Coarse Category | Optical Data Fusion | Multimodal Data Fusion | ||||
---|---|---|---|---|---|---|
Fusion problem | MS-PAN | HS-PAN | HS-MS | Optical-SAR | LWIR-HS-RGB | DEM-MS |
Evaluation scenario | Semi-real | Synthetic | Synthetic | Real | Real | Semi-real |
Quality indices | PSNR, SAM, ERGAS, | — | — | Q index |
Data Set | WorldView-3 Sydney | |||||||
GSD Ratio | 4 | 8 | ||||||
Method | PSNR | SAM | ERGAS | PSNR | SAM | ERGAS | ||
GSA | 30.5889 | 7.0639 | 4.8816 | 0.84731 | 29.5442 | 8.9376 | 2.7818 | 0.80189 |
SFIM | 30.284 | 7.4459 | 4.9078 | 0.80717 | 29.0397 | 9.3161 | 2.8346 | 0.75794 |
GLP | 30.0165 | 7.5339 | 5.0067 | 0.819 | 28.634 | 9.7685 | 2.9399 | 0.76188 |
TGMS | 30.5383 | 7.061 | 4.8447 | 0.84063 | 29.3084 | 8.8521 | 2.7895 | 0.79366 |
Data Set | WorldView-3 Fukushima | |||||||
GSD Ratio | 4 | 8 | ||||||
Method | PSNR | SAM | ERGAS | PSNR | SAM | ERGAS | ||
GSA | 35.2828 | 3.5409 | 2.1947 | 0.86497 | 32.6051 | 5.3814 | 1.5341 | 0.7814 |
SFIM | 34.4099 | 3.5878 | 2.2865 | 0.82623 | 31.9744 | 5.1626 | 1.5426 | 0.7534 |
GLP | 34.9059 | 3.4938 | 2.162 | 0.84492 | 32.1053 | 5.2448 | 1.5273 | 0.76752 |
TGMS | 35.2873 | 3.2785 | 2.0986 | 0.86442 | 32.5916 | 4.9253 | 1.4623 | 0.78618 |
ROSIS University of Pavia | Hyperspec-VNIR Chikusei | |||||||
---|---|---|---|---|---|---|---|---|
Method | PSNR | SAM | ERGAS | Q | PSNR | SAM | ERGAS | Q |
GSA | 31.085 | 6.8886 | 3.6877 | 0.63454 | 33.8284 | 6.9878 | 4.7225 | 0.81024 |
SFIM | 31.0686 | 6.7181 | 3.6715 | 0.60115 | 34.5728 | 6.409 | 4.3559 | 0.84793 |
GLP | 31.6378 | 6.5862 | 3.4586 | 0.6462 | 33.9539 | 7.201 | 4.6249 | 0.81834 |
TGMS | 31.8983 | 6.2592 | 3.3583 | 0.6541 | 35.3262 | 6.1197 | 4.0381 | 0.86051 |
AVIRIS Indian Pines | AVIRIS Cuprite | |||||||
---|---|---|---|---|---|---|---|---|
Method | PSNR | SAM | ERGAS | Q | PSNR | SAM | ERGAS | Q |
GSA | 40.0997 | 0.96775 | 0.44781 | 0.95950 | 39.2154 | 0.98265 | 0.37458 | 0.98254 |
SFIM-HS | 40.7415 | 0.84069 | 0.40043 | 0.91297 | 40.8674 | 0.79776 | 0.31375 | 0.97017 |
GLP-HS | 41.2962 | 0.82635 | 0.37533 | 0.95236 | 40.8240 | 0.80250 | 0.31570 | 0.97838 |
TGMS | 40.8867 | 0.83001 | 0.39279 | 0.9187 | 40.9704 | 0.78922 | 0.30984 | 0.97852 |
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Yokoya, N. Texture-Guided Multisensor Superresolution for Remotely Sensed Images. Remote Sens. 2017, 9, 316. https://doi.org/10.3390/rs9040316
Yokoya N. Texture-Guided Multisensor Superresolution for Remotely Sensed Images. Remote Sensing. 2017; 9(4):316. https://doi.org/10.3390/rs9040316
Chicago/Turabian StyleYokoya, Naoto. 2017. "Texture-Guided Multisensor Superresolution for Remotely Sensed Images" Remote Sensing 9, no. 4: 316. https://doi.org/10.3390/rs9040316