Spectral Reconstruction Methods for Remote Sensing Images: Comparison
View Latest Version
Please note this is a comparison between Version 2 by Sirius Huang and Version 1 by Kai Mu.

Spectral reconstruction of remote sensing images mainly focused on RGB or multispectral to hyperspectral. Spectral reconstruction methods can be divided into two branches: prior-driven and data-driven methods. Earlier researchers adopted the sparse dictionary method. With the development of deep learning, owing to its excellent feature extraction and reconstruction capabilities, more and more researchers are adopting deep learning methods to gradually replace the traditional sparse dictionary approach.

  • spectral reconstruction
  • GF-1
  • GF-6
  • remote sensing
  • deep learning

1. Introduction

The GF-6 was successfully launched in 2018 as China’s first medium-high-resolution agricultural observation satellite, which cooperated with GF-1, China’s first high-resolution earth observation satellite that was launched in 2013. It can not only reduce the time of remote sensing data acquisition from 4 days to 2, but also significantly improve the ability to monitor agriculture, forestry, grassland, and other resources, providing remote sensing data support for agricultural and rural development, ecological civilization construction [1], and other significant needs. GF-6 also realized the localization of the 8-band CMOS detector and added the red-edge band that can effectively reflect the unique spectral characteristics of crops [2,3][2][3].
However, GF-1 was launched earlier and is mission-oriented differently, so it only contains four multispectral bands. Compared with the GF-6 satellite in Table 1, GF-1 lacks four bands (purple, yellow, red-edge I, and red-edge II bands), which greatly constrains its development for crop-related joint monitoring. Therefore, researchers try to find a spectral reconstruction method to reconstruct the lacking four bands.
Table 1.
 Band specification of the GF-1 PMS and GF-6 WFV images.
GF-1 PMS GF-6 WFV
Band Wavelength (nm) Spatial

Resolution (m)		 Band Wavelength (nm) Spatial

Resolution (m)
Blue 450∼520 8 Blue 450∼520 16
Green 520∼590 8 Green 520∼590 16
Red 630∼690 8 Red 630∼690 16
Nir 730∼890 8 Nir 730∼890 16
      Red edge 1 690∼730 16
      Red edge 1 730∼770 16
      Purple 400∼450 16
      Yellow 590∼640 16
Pan 450∼900 2      
In recent years, spectral reconstruction mainly focused on RGB or multispectral to hyperspectral. Earlier researchers adopted the sparse dictionary method [4,5,6,7,8,9][4][5][6][7][8][9]. With the development of deep learning, owing to its excellent feature extraction and reconstruction capabilities, more and more researchers are adopting deep learning methods to gradually replace the traditional sparse dictionary approach [10,11,12,13,14,15,16][10][11][12][13][14][15][16].
In addition, it should be pointed out that most studies on spectral reconstruction focus on visible three bands (red, green, and blue) images, while remote sensing images usually contain at least four bands (red, green, blue, and nir). This results in the lack of one essential nir band as the input, which does not make full use of the original information, thereby leading to a waste of information. There are already some studies of remote sensing spectral reconstruction considering this problem [15,16][15][16]. Few studies have been conducted on large-scale and highly complex scenarios such as satellite remote sensing. On the contrary, most of them have only done performed research in a relatively small area [15]. Most deep learning methods adopt a lot of up-sampling, down-sampling, and nonlocal attention structure for ground images. Due to the large-scale, numerous, and complex ground objects of remote sensing images, these structures are difficult to play an excellent effect in the spectral reconstruction of remote sensing images [16].

2. Spectral Reconstruction Methods for Remote Sensing Images

Due to the limitations of the hardware resources (bandwidth and sensors), researchers have had to make trade-offs in the temporal, spatial, and spectral dimensions of remote sensing images. With the problem of low spectral dimension, researchers mainly used principal component analysis (PCA) [17[17][18],18], Wiener estimation (WEN) [19], and pseudoinverse (PI) [20,21][20][21] to construct a spectral mapping matrix. In recent years, spectral reconstruction methods have been divided into two branches: prior-driven and data-driven methods. The first type is mainly based on sparse dictionary learning, which aims to extract the most important spectral mapping features. It can represent as much knowledge as possible with as few resources as possible, and this representation has the added benefit of being computationally fast. For example, Arad and Ben-Shahar [4] were the first to apply an overcomplete dictionary to recover hyperspectral images from RGB. Jonas et al. [5] used the A+ algorithm to improve Arad’s approach to the sparse dictionary. The A+ algorithm directly constructs the mapping from RGB to hyperspectral at the local anchor point, and the running speed of the algorithm is significantly improved. The sparse dictionary method only considers the sparsity of spectral information and does not use local linearity. The disadvantage is that the reconstruction is inaccurate, and the reconstructed image has metamerism [22]. Li et al. [7] proposed a locally linear embedding sparse dictionary method to improve the representation ability of sparse coding. In order to improve the representation ability of the sparse dictionary, this method only selects the local best samples and introduces texture information in the reconstruction, reducing the metamerism. Geng et al. [8] proposed a spectral reconstruction method that preserves contextual information. Gao et al. [9] performed spectral enhancement of multispectral images by jointly learning low-rank dictionary pairs from overlapping regions. The second type is mainly based on deep learning. With the development of deep learning, a large number of excellent models have gradually replaced the first method owing to its powerful generalization ability. However, compared to the first one, deep learning usually requires enormous amounts of data, and the training process takes a lot of computational time. However, with the increase in computing power, deep learning becomes much more effective, and the related methods are used by more and more researchers. Xiong et al. [10] proposed a deep learning framework for recovering spectral information from spectrally undersampled images. Koundinya et al. [12] compared 2D and 3D kernel-based CNN for spectral reconstruction. Alvarez-Gila et al. [11] posed spectral reconstruction as an image-to-image mapping problem and proposed a generative adversarial networks for spatial context-aware spectral image reconstruction. In the NTIRE 2018 [23] first spectral reconstruction challenge, the entries of Shi et al. [13] ranked in first (HSCNN-D) and second (HSCNN-R) place on both the “Clean” and “Real World” tracks. The main difference between the two networks is that the former adopts a series method for feature fusion, while the latter is an addition method. The series method can learn the mapping relationship between spectra very well. Respectively considering shallow feature extraction and deep feature extraction, Li et al. [24] proposed an adaptive weighted attention network, which obtained the first rank on the “Clean” track. Zhao et al. [14] proposed a hierarchical regression network (HRNet) that obtained first place on the “Real World” track; it is a 4-level multi-scale structure that uses down-sampling and up-sampling to extract spectral features. In the processing of remote sensing images, Deng et al. [15] proposed a more suitable network (M2H-Net) for remote sensing to meet the needs of multiple bands and complex scenes. Li and Gu [16] proposed a progressive spatial–-spectral joint network for hyperspectral image reconstruction.

References

  1. Wu, Z.; Zhang, J.; Deng, F.; Zhang, S.; Zhang, D.; Xun, L.; Javed, T.; Liu, G.; Liu, D.; Ji, M. Fusion of GF and MODIS Data for Regional-Scale Grassland Community Classification with EVI2 Time-Series and Phenological Features. Remote Sens. 2021, 13, 835.
  2. Jiang, X.; Fang, S.; Huang, X.; Liu, Y.; Guo, L. Rice Mapping and Growth Monitoring Based on Time Series GF-6 Images and Red-Edge Bands. Remote Sens. 2021, 13, 579.
  3. Kang, Y.; Hu, X.; Meng, Q.; Zou, Y.; Zhang, L.; Liu, M.; Zhao, M. Land Cover and Crop Classification Based on Red Edge Indices Features of GF-6 WFV Time Series Data. Remote Sens. 2021, 13, 4522.
  4. Arad, B.; Ben-Shahar, O. Sparse recovery of hyperspectral signal from natural RGB images. In Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands, 11–14 October 2016; pp. 19–34.
  5. Aeschbacher, J.; Wu, J.; Timofte, R. In defense of shallow learned spectral reconstruction from RGB images. In Proceedings of the IEEE International Conference on Computer Vision Workshops, Venice, Italy, 22–29 October 2017; pp. 471–479.
  6. Fu, Y.; Zheng, Y.; Zhang, L.; Huang, H. Spectral Reflectance Recovery From a Single RGB Image. IEEE Trans. Comput. Imaging 2018, 4, 382–394.
  7. Li, Y.; Wang, C.; Zhao, J. Locally Linear Embedded Sparse Coding for Spectral Reconstruction From RGB Images. IEEE Signal Process. Lett. 2018, 25, 363–367.
  8. Geng, Y.; Mei, S.; Tian, J.; Zhang, Y.; Du, Q. Spatial Constrained Hyperspectral Reconstruction from RGB Inputs Using Dictionary Representation. In Proceedings of the IGARSS 2019–2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 28 July–2 August 2019; pp. 3169–3172.
  9. Gao, L.; Hong, D.; Yao, J.; Zhang, B.; Gamba, P.; Chanussot, J. Spectral superresolution of multispectral imagery with joint sparse and low-rank learning. IEEE Trans. Geosci. Remote Sens. 2020, 59, 2269–2280.
  10. Xiong, Z.; Shi, Z.; Li, H.; Wang, L.; Liu, D.; Wu, F. Hscnn: Cnn-based hyperspectral image recovery from spectrally undersampled projections. In Proceedings of the IEEE International Conference on Computer Vision Workshops, Venice, Italy, 22–29 October 2017; pp. 518–525.
  11. Alvarez-Gila, A.; Van De Weijer, J.; Garrote, E. Adversarial networks for spatial context-aware spectral image reconstruction from rgb. In Proceedings of the IEEE International Conference on Computer Vision Workshops, Venice, Italy, 22–29 October 2017; pp. 480–490.
  12. Koundinya, S.; Sharma, H.; Sharma, M.; Upadhyay, A.; Manekar, R.; Mukhopadhyay, R.; Karmakar, A.; Chaudhury, S. 2D-3D CNN based architectures for spectral reconstruction from RGB images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA, 18–23 June 2018; pp. 844–851.
  13. Shi, Z.; Chen, C.; Xiong, Z.; Liu, D.; Wu, F. Hscnn+: Advanced cnn-based hyperspectral recovery from rgb images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA, 18–23 June 2018; pp. 939–947.
  14. Zhao, Y.; Po, L.M.; Yan, Q.; Liu, W.; Lin, T. Hierarchical regression network for spectral reconstruction from RGB images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA, 14–19 June 2020; pp. 422–423.
  15. Deng, L.; Sun, J.; Chen, Y.; Lu, H.; Duan, F.; Zhu, L.; Fan, T. M2H-Net: A Reconstruction Method For Hyperspectral Remotely Sensed Imagery. ISPRS J. Photogramm. Remote Sens. 2021, 173, 323–348.
  16. Li, T.; Gu, Y. Progressive Spatial–Spectral Joint Network for Hyperspectral Image Reconstruction. IEEE Trans. Geosci. Remote Sens. 2021, 60, 1–14.
  17. Zhang, X.; Xu, H. Reconstructing spectral reflectance by dividing spectral space and extending the principal components in principal component analysis. J. Opt. Soc. Am. A 2008, 25, 371–378.
  18. Liu, X.; Liu, L. Improving chlorophyll fluorescence retrieval using reflectance reconstruction based on principal components analysis. IEEE Geosci. Remote Sens. Lett. 2015, 12, 1645–1649.
  19. Haneishi, H.; Hasegawa, T.; Hosoi, A.; Yokoyama, Y.; Tsumura, N.; Miyake, Y. System design for accurately estimating the spectral reflectance of art paintings. Appl. Opt. 2000, 39, 6621–6632.
  20. Imai, F.H.; Berns, R.S. Spectral estimation using trichromatic digital cameras. In Proceedings of the International Symposium on Multispectral Imaging and Color Reproduction for Digital Archives, Chiba, Japan, 21–22 October 1999; Volume 42, pp. 1–8.
  21. Cheung, V.; Westland, S.; Li, C.; Hardeberg, J.; Connah, D. Characterization of trichromatic color cameras by using a new multispectral imaging technique. JOSA A 2005, 22, 1231–1240.
  22. Zhang, J.; Su, R.; Ren, W.; Fu, Q.; Nie, Y. Learnable Reconstruction Methods from RGB Images to Hyperspectral Imaging: A Survey. arXiv 2021, arXiv:2106.15944.
  23. Arad, B.; Ben-Shahar, O.; Timofte, R.; Gool, L.V.; Yang, M.H. NTIRE 2018 Challenge on Spectral Reconstruction from RGB Images. In Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA, 18–23 June 2018.
  24. Li, J.; Wu, C.; Song, R.; Li, Y.; Liu, F. Adaptive weighted attention network with camera spectral sensitivity prior for spectral reconstruction from RGB images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA, 14–19 June 2020; pp. 462–463.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)
ScholarVision Creations
Feedback