Sparse Representing Denoising of Hyperspectral Data for Water Color Remote Sensing
Source abstract
Abstract
Hyperspectral data are important for water color remote sensing. The inevitable noise will devalue its application. In this study, we developed a 1-D denoising method for water hyperspectral data, based on sparse representing. The denoising performance was compared with three commonly used methods in simulated and real datasets. The results indicate that: (1) sparse representing can successfully decompose the hyperspectral water-surface reflectance signal from random noises; (2) the proposed method exhibited better performance compared with the other three methods in different input signal-to-noise ratio (SNR) levels; (3) the proposed method effectively erased abnormal spectral vibrations of field-measured and remote-sensing hyperspectral data; (4) whilst the method is built in 1-D, it can still control the salt-and-pepper noise of PRISMA hyperspectral image. In conclusion, the proposed denoising method can improve the hyperspectral data of an optically complex water body and offer a better data source for the remote monitoring of water color.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Image and Signal Denoising Methods · Remote-Sensing Image Classification · Advanced Image Fusion Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of the Resources and Environmental Sciences, Henan Agricultural University, Zhengzhou 450002, China
Human Engineering Research Center of Land Consolidation and Ecological Restoration, Henan Agricultural University, Zhengzhou 450002, China
School of Tourism and Urban & Rural Planning, Zhejiang Gongshang University, Hangzhou 310018, China
Jiangsu Collaborative Innovation Center of Regional Modern Agriculture & Environmental Protection, Huaiyin Normal University, Huai’an 223000, China
Funding
National Key R&D Program of China · National Natural Science Foundation of China · Young backbone teachers program of Henan Province
References (72)
Show source-provided references
1. Carder Absorption spectrum of phytoplankton pigments derived from hyperspectral remote sensing reflectance10.1016/j.rse.2003.10.013 ↗
2. Sensing ocean plastics with an airborne hyperspectral shortwave infrared imager
3. Hyperspectral remote sensing of total phosphorus (TP) in three central Indiana water supply reservoirs10.1007/s11270-011-0959-6 ↗
4. The Hyperspectral Data Monitoring Model of Chlorophyll-a of Summer in Taihu Lake, China
5. Hyperspectral retrievals of phytoplankton absorption and chlorophyll-a in inland and nearshore coastal waters10.1016/j.rse.2020.112200 ↗
6. HYDROPT: A fast and flexible method to retrieve chlorophyll-a from multispectral satellite observations of optically complex coastal waters10.1016/j.rse.2007.09.001 ↗
7. The water color simulator WASI: An integrating software tool for analysis and simulation of optical in situ spectra10.1016/j.cageo.2004.03.005 ↗
8. Optical remote sensing techniques to estimate phytoplankton chlorophyll a concentrations in coastal waters with varying suspended matter and cdom concentrations
9. Spectral interdependence of remote-sensing reflectance and its implications on the design of ocean color satellite sensors10.1364/AO.53.003301 ↗
10. Reconstruction of hyperspectral reflectance for optically complex turbid inland lakes: Test of a new scheme and implications for inversion algorithms10.1364/OE.23.00A718 ↗
11. Chlorophyll measurements in the presence of suspended sediment using broad band spectral sensors aboard satellites10.1080/02705060.1994.9664887 ↗
12. Effect of Bio-Optical Parameter Variability and Uncertainties in Reflectance Measurements on the Remote Estimation of Chlorophyll-a Concentration in Turbid Productive Waters: Modeling Results
13. Zhang, R., Zheng, Z., Liu, G., Du, C., Du, C., Lei, S., Xu, Y., Xu, J., Mu, M., and Bi, S. (2021). Simulation and assessment of the capabilities of orbita hyperspectral (OHS) imagery for remotely monitoring chlorophyll-a in eutrophic plateau lakes. Remote Sens., 13.10.3390/rs13142821 ↗
14. A Four-Band Semi-Analytical Model for Estimating Phycocyanin in Inland Waters from Simulated MERIS and OLCI Data10.1109/TGRS.2017.2761996 ↗
15. HICO-based NIR-red models for estimating chlorophyll-a concentration in productive coastal waters10.1109/LGRS.2013.2287458 ↗
16. Estimation of the remote-sensing reflectance from above-surface measurements10.1364/AO.38.007442 ↗
17. Remote sensing reflectance and inherent optical properties of oceanic waters derived from above-water measurements10.1117/12.266436 ↗
18. Remote estimation of chlorophyll-a concentration in turbid water using a spectral index: A case study in Taihu Lake, China10.1117/1.JRS.7.073465 ↗
19. Noise Removal in Spectrum Above Water Surface Using Kernel Regression Smoothing
20. Estimation of chlorophyll-a concentration in Turbid Lake using spectral smoothing and derivative analysis10.3390/ijerph10072979 ↗
21. Denoising of hyperspectral imagery using principal component analysis and wavelet shrinkage10.1109/TGRS.2010.2075937 ↗
22. The Application of Multiwavelet Filter Banks to Image Processing10.1109/83.753742 ↗
23. Hyperspectral image denoising via sparse representation and low-rank constraint10.1109/TGRS.2014.2321557 ↗
24. Hyperspectral Image Denoising Using Spatio-Spectral Total Variation
25. Hyperspectral Image Denoising Employing a Spatial-Spectral Deep Residual Convolutional Neural Network10.1109/TGRS.2018.2865197 ↗
26. An efficient image denoising method based on principal component analysis with learned patch groups10.1007/s11760-019-01489-2 ↗
27. Two-stage image denoising by principal component analysis with local pixel grouping10.1016/j.patcog.2009.09.023 ↗
28. Novel design of a fractional wavelet and its application to image denoising10.11591/eei.v9i1.1548 ↗
29. Li, H., and Wang, S. (2009, January 15–17). A new image denoising method using wavelet transform. Proceedings of the Proceedings—2009 International Forum on Information Technology and Applications, IFITA 2009, Chengdu, China.
30. Rudin, L.I., Osher, S., and Fatemi, E. (1992). Nonlinear Total Variation Based Noise Removal Algorithms, Elsevier.10.1016/0167-2789(92)90242-F ↗
31. Iterative Methods for Total Variation Denoising10.1137/0917016 ↗
32. Spectral-Spatial Adaptive Sparse Representation for Hyperspectral Image Denoising10.1109/TGRS.2015.2457614 ↗
33. Elad, M., and Aharon, M. (2006, January 17–22). Image Denoising Via Learned Dictionaries and Sparse Representation. Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), New York, NY, USA.
34. Hyperspectral Image Denoising via Noise-Adjusted Iterative Low-Rank Matrix Approximation10.1109/JSTARS.2015.2398433 ↗
35. Deep Learning on Image Denoising: An overview10.1016/j.neunet.2020.07.025 ↗
36. Ahmadi, B., Gholamalifard, M., Kutser, T., Vignudelli, S., and Kostianoy, A. (2020). Spatio-temporal variability in bio-optical properties of the southern caspian sea: A historic analysis of ocean color data. Remote Sens., 12.10.3390/rs12233975 ↗
37. El Ouali, A., El Hafyani, M., Roubil, A., Lahrach, A., Essahlaoui, A., Hamid, F.E., Muzirafuti, A., Paraforos, D.S., Lanza, S., and Randazzo, G. (2021). Modeling and spatiotemporal mapping of water quality through remote sensing techniques: A case study of the hassan addakhil dam. Appl. Sci., 11.10.3390/app11199297 ↗
38. Du, C., Li, Y., Lyu, H., Shi, K., Liu, N., Yan, C., Pan, J., Guo, Y., and Li, Y. (2022). Characteristics of the Total Suspended Matter Concentration in the Hongze Lake during 1984–2019 Based on Landsat Data. Remote Sens., 14.10.3390/rs14122919 ↗
39. A semi-analytical approach for detecting suspended particulate composition in complex turbid inland waters (China)10.1016/j.rse.2013.02.024 ↗
40. A Novel Multitemporal Image-Fusion Algorithm: Method and Application to GOCI and Himawari Images for Inland Water Remote Sensing10.1109/TGRS.2019.2960322 ↗
41. Analysis of water clarity decrease in Xin’anjiang Reservoir, China, from 30-Year Landsat TM, ETM+, and OLI observations10.1016/j.jhydrol.2020.125476 ↗
42. Modeling the tidal channel morphodynamics in a macro-tidal embayment, Hangzhou Bay, China10.1016/j.csr.2009.03.009 ↗
43. Liu, J., Zhu, Q., Gong, F., He, X., Chen, J., and Huang, H. (2014, January 8). Retrieval of total suspended particulate matter in highly turbid waters of Hangzhou Bay using polarizing spectra data. Proceedings of the Remote Sensing and Modeling of Ecosystems for Sustainability XI, SPIE, San Diego, CA, USA.10.1117/12.2061310 ↗
44. Mueller, J.L., Fargion, G.S., Mcclain, C.R., Mueller, J.L., Morel, A., Frouin, R., Davis, C., Arnone, R., Carder, K., and Lee, Z.P. (2003). NASA/TM-2003-21621/Rev-Vol III Ocean Optics Protocols For Satellite Ocean Color Sensor Validation, Revision 4, Volume III: Radiometric Measurements and Data Analysis Protocols, NASA.
45. Optical types of inland and coastal waters10.1002/lno.10674 ↗
46. The PRISMA imaging spectroscopy mission: Overview and first performance analysis10.1016/j.rse.2021.112499 ↗
47. Niroumand-Jadidi, M., Bovolo, F., and Bruzzone, L. (2020). Water quality retrieval from PRISMA hyperspectral images: First experience in a turbid lake and comparison with sentinel-2. Remote Sens., 12.10.3390/rs12233984 ↗
48. Hyperspectral reconstruction method for optically complex inland waters based on bio-optical model and sparse representing10.1016/j.rse.2022.113045 ↗
49. An Improved Land Target-Based Atmospheric Correction Method for Lake Taihu10.1109/JSTARS.2015.2503800 ↗
50. Bi, S., Li, Y., Wang, Q., Lyu, H., Liu, G., Zheng, Z., Du, C., Mu, M., Xu, J., and Lei, S. (2018). Inland water Atmospheric Correction based on Turbidity Classification using OLCI and SLSTR synergistic observations. Remote Sens., 10.10.3390/rs10071002 ↗
51. A simple atmospheric correction algorithm for MODIS in shallow turbid waters: A case study in taihu lake10.1109/JSTARS.2012.2224849 ↗
52. Remote sensing of water optical property for China’s inland lake taihu using the SWIR atmospheric correction with 1640 and 2130 nm bands10.1109/JSTARS.2013.2243820 ↗
53. Using Spectral Smoothing Method to Improve the Validation Precision of the Chlorophyll-a Estimation Model in Turbidity Water
54. Estimation of chlorophyll a concentration using NIR/Red bands of MERIS and classification procedure in inland turbid water10.1109/TGRS.2011.2163199 ↗
55. Assessment of NIR-red algorithms for observation of chlorophyll-a in highly turbid inland waters in China10.1016/j.isprsjprs.2014.03.012 ↗
56. Analysis of MERIS reflectance algorithms for estimating chlorophyll-a concentration in a Brazilian reservoir10.3390/rs61211689 ↗
57. A four-band semi-analytical model for estimating chlorophyll a in highly turbid lakes: The case of Taihu Lake, China10.1016/j.rse.2009.02.005 ↗
58. Three-Dimensional Variation in Light Quality in the Upper Water Column Revealed With a Single Parameter
59. An Inversion-Based Fusion Method for Inland Water Remote Monitoring10.1109/JSTARS.2016.2615125 ↗
60. Quantification of lake clarity in China using Landsat OLI imagery data10.1016/j.rse.2020.111800 ↗
61. Bathymetric Retrieval Selectively Using Multiangular High-Spatial-Resolution Satellite Imagery10.1109/JSTARS.2020.3040186 ↗
62. Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach10.1016/j.rse.2019.111604 ↗
63. Trophic state assessment of global inland waters using a MODIS-derived Forel-Ule index10.1016/j.rse.2018.08.026 ↗
64. Modeling suspended sediment distribution patterns of the Amazon River using MODIS data10.1016/j.rse.2014.03.013 ↗
65. Can MODIS Land Reflectance Products be Used for Estuarine and Inland Waters?10.1029/2017WR021607 ↗
66. Remote sensing chlorophyll a of optically complex waters (rias Baixas, NW Spain): Application of a regionally specific chlorophyll a algorithm for MERIS full resolution data during an upwelling cycle10.1016/j.rse.2011.05.008 ↗
67. Medium resolution imaging spectrometer (MERIS) estimation of chlorophyll-a concentration in the turbid sediment-laden waters of the Changjiang (Yangtze) Estuary10.1080/01431161.2010.485216 ↗
68. A novel MERIS algorithm to derive cyanobacterial phycocyanin pigment concentrations in a eutrophic lake: Theoretical basis and practical considerations10.1016/j.rse.2014.08.026 ↗
69. Satellite observation of hourly dynamic characteristics of algae with Geostationary Ocean Color Imager (GOCI) data in Lake Taihu10.1016/j.rse.2014.12.016 ↗
70. An Expanded Three Band Model to Monitor Inland Optically Complex Water Using Geostationary Ocean Color Imager (GOCI)10.3389/frsen.2022.803884 ↗
71. Estimation of Chlorophyll-a Concentrations in Small Water Bodies: Comparison of Fused Gaofen-6 and Sentinel-2 Sensors10.3390/rs14010229 ↗
72. Remote Sensing Monitoring of Taihu Lake water Quality by using GF-1 satellite WFV data