192
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Multi-temporal grass hyperspectral classification via full pixel decomposition spectral manifold projection and boosting active learning model

ORCID Icon, , &

References

  • Boukhechba, K., H. Wu, and R. Bazine. 2018. “DCT-Based Preprocessing Approach for ICA in Hyperspectral Data Analysis.” Sensors 18 (4): 1138. doi:10.3390/s18041138.
  • Cao, X., J. Yao, X. Fu, H. Bi, and D. Hong. 2021. “An Enhanced 3-D Discrete Wavelet Transform for Hyperspectral Image Classification.” IEEE Geoscience and Remote Sensing Letters 18 (6): 1104–1108. doi:10.1109/LGRS.2020.2990407.
  • Carstairs, H., E. Mitchard, I. McNicol, C. Aquino, A. Burt, M. Ebanega, A. Dikongo, J. Bueso-Bello, and M. Disney. 2022. “An Effective Method for InSar Mapping of Tropical Forest Degradation in Hilly Areas.” Remote Sensing 14 (3): 452. doi:10.3390/rs14030452.
  • Chandra, A., S. Desai, W. Guo, and V. Balasubramanian, 2020. Computer Vision with Deep Learning for Plant Phenotyping in Agriculture: A Survey. Advanced Computing and Communications arXiv e-prints. arXiv:2006.11391. 10.34048/ACC.2020.1.F1
  • Chen, T., and C. Guestrin, 2016. “XGBoost: A Scalable Tree Boosting System.” KDD ’16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. 10.1145/2939672.2939785
  • Chen, Y., S. Ma, H. Jiang, Y. Hu, and X. Lu. 2020a. “Influences of Litter Diversity and Soil Moisture on Soil Microbial Communities in Decomposing Mixed Litter of Alpine Steppe Species.” Geoderma 377 (July): 114577. doi:10.1016/j.geoderma.2020.114577.
  • Chen, L., F. Xia, M. Wang, W. Wang, and P. Mao. 2020b. “Metabolomic Analyses of Alfalfa (Medicago Sativa L. Cv. ‘Aohan’) Reproductive Organs Under Boron Deficiency and Surplus Conditions.” Ecotoxicology and Environmental Safety 202: 111011. doi:10.1016/j.ecoenv.2020.111011.
  • Clark, C., and D. Tilman. 2008. “Loss of Plant Species After Chronic Low-Level Nitrogen Deposition to Prairie Grasslands.” Nature 451 (7179): 712–715. doi:https://doi.org/10.1038/nature06503.
  • Cui, Y., W. Ge, J. Li, J. Zhang, D. An, and Y. Wei. 2019. “Screening of Maize Haploid Kernels Based on Near Infrared Spectroscopy Quantitative Analysis.” Computers and Electronics in Agriculture 158: 358–368. doi:10.1016/j.compag.2019.01.038.
  • Dabija, A., M. Kluczek, B. Zagajewski, E. Raczko, M. Kycko, A. H. Al-Sulttani, A. Tardà, L. Pineda, and J. Corbera. 2021. “Comparison of Support Vector Machines and Random Forests for Corine Land Cover Mapping.” Remote Sensing 13 (4): 777. doi:https://doi.org/10.3390/rs13040777.
  • Dao, P., A. Axiotis, and Y. He. 2021. “Mapping Native and Invasive Grassland Species and Characterizing Topography-Driven Species Dynamics Using High Spatial Resolution Hyperspectral Imagery.” International Journal of Applied Earth Observation and Geoinformation 104: 102542. doi:10.1016/j.jag.2021.102542.
  • Demarchi, L., A. Kania, W. Ciężkowski, H. Piórkowski, Z. Oświecimska-Piasko, and J. Chormański. 2020. “Recursive Feature Elimination and Random Forest Classification of Natura 2000 Grasslands in Lowland River Valleys of Poland Based on Airborne Hyperspectral and LiDar Data Fusion.” Remote Sensing 12 (11): 1842. doi:10.3390/rs12111842.
  • Feilhauer, H., U. Faude, and S. Schmidtlein. 2011. “Combining Isomap Ordination and Imaging Spectroscopy to Map Continuous Floristic Gradients in a Heterogeneous Landscape.” Remote Sensing of Environment 115 (10): 2513–2524. doi:10.1016/j.rse.2011.05.011.
  • Gholizadeh, H., J. Gamon, A. Zygielbaum, R. Wang, A. Schweiger, and J. Cavender-Bares. 2018. “Remote Sensing of Biodiversity: Soil Correction and Data Dimension Reduction Methods Improve Assessment of α-Diversity (Species Richness) in Prairie Ecosystems.” Remote Sensing of Environment 206: 240–253. doi:10.1016/j.rse.2017.12.014.
  • Hajjar, S., F. Dornaika, and F. Abdallah. 2022. “Multi-View Spectral Clustering via Constrained Nonnegative Embedding.” Information Fusion 78: 209–217. doi:10.1016/j.inffus.2021.09.009.
  • Huang, H., Z. Li, H. He, Y. Duan, and S. Yang. 2020. “Self-Adaptive Manifold Discriminant Analysis for Feature Extraction from Hyperspectral Imagery.” Pattern recognition 107: 107487. doi:10.1016/j.patcog.2020.107487.
  • Hu, Z., F. Nie, R. Wang, and X. Li. 2020. “Multi-View Spectral Clustering via Integrating Nonnegative Embedding and Spectral Embedding.” Information Fusion 55: 251–259. doi:10.1016/j.inffus.2019.09.005.
  • Kluczek, M., B. Zagajewski, and M. Kycko. 2022. “Airborne HySpex Hyperspectral versus Multitemporal Sentinel-2 Images for Mountain Plant Communities Mapping.” Remote Sensing 14 (5): 1209. doi:https://doi.org/10.3390/rs14051209.
  • Krause, B., and H. Culmsee. 2013. “The Significance of Habitat Continuity and Current Management on the Compositional and Functional Diversity of Grasslands in the Uplands of Lower Saxony, Germany.” Flora-Morphology, Distribution, Functional Ecology of Plants 208 (5–6): 299–311. doi:10.1016/j.flora.2013.04.003.
  • Kumar, P., and A. Gupta. 2020. “Active Learning Query Strategies for Classification, Regression, and Clustering: A Survey.” Journal of Computer Science and Technology 35 (4): 913–945. doi:https://doi.org/10.1007/s11390-020-9487-4.
  • Li, W., F. Feng, H. Li, and Q. Du. 2018a. “Discriminant Analysis-Based Dimension Reduction for Hyperspectral Image Classification: A Survey of the Most Recent Advances and an Experimental Comparison of Different Techniques.” IEEE Geoscience and Remote Sensing Magazine 6 (1): 15–34. doi:10.1109/MGRS.2018.2793873.
  • Li, S., Q. Hao, G. Gao, and X. Kang. 2018b. “The Effect of Ground Truth on Performance Evaluation of Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 56 (12): 7195–7206. doi:10.1109/TGRS.2018.2849225.
  • Lin, X., H. Zhao, S. Zhang, X. Li, W. Gao, Z. Ren, and M. Luo. 2021. “Effects of Animal Grazing on Vegetation Biomass and Soil Moisture on a Typical Steppe in Inner Mongolia, China.” Ecohydrology 15 (1). doi:10.1002/eco.2350.
  • Liu, H., Q. Li, Y. Bai, C. Yang, J. Wang, Q. Zhou, S. Hu, T. Shi, X. Liao, and G. Wu. 2021. “Improving Satellite Retrieval of Oceanic Particulate Organic Carbon Concentrations Using Machine Learning Methods.” Remote Sensing of Environment 256: 112316. doi:10.1016/j.rse.2021.112316.
  • Liu, H., K. Xia, T. Li, J. Ma, and E. Owoola. 2020. “Dimensionality Reduction of Hyperspectral Images Based on Improved Spatial–Spectral Weight Manifold Embedding.” Sensors 20 (16): 4413. doi:10.3390/s20164413.
  • Li, Z., Z. Wang, Z. Qu, Y. Na, Z. Wang, S. Lv, H. Wang, and G. Han. 2021. “Review of Grassland Classification and Grading and Construction of a New System in China.” Resources Science 43 (11): 2192–2202. doi:10.18402/resci.2021.11.04.
  • Lyu, X., X. Li, D. Dang, H. Dou, X. Xuan, S. Liu, M. Li, and J. Gong. 2020. “A New Method for Grassland Degradation Monitoring by Vegetation Species Composition Using Hyperspectral Remote Sensing.” Ecological indicators 114: 106310. doi:10.1016/j.ecolind.2020.106310.
  • Marcinkowska-Ochtyra, A., A. Jarocińska, K. Bzdęga, and B. Tokarska-Guzik. 2018. “Classification of Expansive Grassland Species in Different Growth Stages Based on Hyperspectral and LiDar Data.” Remote Sensing 10 (12): 2019. doi:10.3390/rs10122019.
  • Möckel, T., J. Dalmayne, H. Prentice, L. Eklundh, O. Purschke, S. Schmidtlein, and K. Hall. 2014. “Classification of Grassland Successional Stages Using Airborne Hyperspectral Imagery.” Remote Sensing 6 (8): 7732–7761. doi:10.3390/rs6087732.
  • Orynbaikyzy, A., U. Gessner, and C. Conrad. 2019. “Crop Type Classification Using a Combination of Optical and Radar Remote Sensing Data: A Review.” International Journal of Remote Sensing 40 (17): 6553–6595. doi:10.1080/01431161.2019.1569791.
  • Pi, W., J. Du, Y. Bi, X. Gao, and X. Zhu. 2021. “3D-CNN Based UAV Hyperspectral Imagery for Grassland Degradation Indicator Ground Object Classification Research.” Ecological informatics 62: 101278. doi:10.1016/j.ecoinf.2021.101278.
  • Purschke, O., B. Schmid, M. Sykes, P. Poschlod, S. Michalski, W. Durka, I. Kühn, M. Winter, H. Prentice, and J. Fridley. 2013. “Contrasting Changes in Taxonomic, Phylogenetic and Functional Diversity During a Long-Term Succession: Insights into Assembly Processes.” The Journal of Ecology 101 (4): 857–866. doi:https://doi.org/10.1111/1365-2745.12098.
  • Pykälä, J., M. Luoto, R. Heikkinen, and T. Kontula. 2005. “Plant Species Richness and Persistence of Rare Plants in Abandoned Semi-Natural Grasslands in Northern Europe.” Basic and Applied Ecology 6 (1): 25–33. doi:https://doi.org/10.1016/j.baae.2004.10.002.
  • Qu, H., L. Li, Z. Li, and J. Zheng. 2021. “Supervised Discriminant Isomap with Maximum Margin Graph Regularization for Dimensionality Reduction.” Expert Systems with Applications 180: 115055. doi:10.1016/j.eswa.2021.115055.
  • Sha, Z., J. Zhong, Y. Bai, X. Tan, and J. Li. 2016. “Spatio-Temporal Patterns of Satellite-Derived Grassland Vegetation Phenology from 1998 to 2012 in Inner Mongolia, China.” Journal of Arid Land 8 (3): 462–477. doi:10.1007/s40333-016-0121-9.
  • Subudhi, S., R. Patro, P. Biswal, and F. Dell’Acqua. 2021. “A Survey on Superpixel Segmentation as a Preprocessing Step in Hyperspectral Image Analysis.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14: 5015–5035. doi:10.1109/jstars.2021.3076005.
  • Wang, M., Y. Zhang, and F. Min. 2019. “Active Learning Through Multi-Standard Optimization.” IEEE Access 7: 56772–56784. doi:10.1109/ACCESS.2019.2914263.
  • Wu, X., Z. Li, B. Fu, W. Zhou, H. Liu, and G. Liu. 2014. “Restoration of Ecosystem Carbon and Nitrogen Storage and Microbial Biomass After Grazing Exclusion in Semi-Arid Grasslands of Inner Mongolia.” Ecological Engineering 73: 395–403. doi:https://doi.org/10.1016/j.ecoleng.2014.09.077.
  • Wu, Z., J. Zhang, F. Deng, S. Zhang, D. Zhang, L. Xun, M. Ji, and Q. Feng. 2021. “Superpixel-Based Regional-Scale Grassland Community Classification Using Genetic Programming with Sentinel-1 SAR and Sentinel-2 Multispectral Images.” Remote Sensing 13 (20): 4067. doi:10.3390/rs13204067.
  • Xiao, B., W. Mao, X. Liang, L. Zhang, and L. Han. 2012. “Study on Varieties Identification of Kentucky Bluegrass Using Hyperspectral Imaging and Discriminant Analysis.” Spectroscopy and Spectral Analysis 32 (06): 1620–1623. doi:10.3964/j.issn.1000-0593(2012)06-1620-04.
  • Xu, H., H. Zhang, W. He, and L. Zhang. 2019. “Superpixel-Based Spatial-Spectral Dimension Reduction for Hyperspectral Imagery Classification.” Neurocomputing 360: 138–150. doi:10.1016/j.neucom.2019.06.023.
  • Xu, M., Q. Zhao, and S. Jia. 2022. “Multiview Spatial–Spectral Active Learning for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 60: 1–15. doi:10.1109/tgrs.2021.3095292.
  • Yang, H., and J. Du. 2021. “Classification of Desert Steppe Species Based on Unmanned Aerial Vehicle Hyperspectral Remote Sensing and Continuum Removal Vegetation Indices.” Optik 247: 167877. doi:10.1016/j.ijleo.2021.167877.
  • Yang, M., D. Xu, S. Chen, H. Li, and Z. Shi. 2019. “Evaluation of Machine Learning Approaches to Predict Soil Organic Matter and pH Using Vis-NIR Spectra.” Sensors 19 (2): 263. doi:https://doi.org/10.3390/s19020263.
  • Zhang, X., H. Liu, X. Wu, X. Zhang, and X. Liu. 2021. “Spectral Embedding Network for Attributed Graph Clustering.” Neural Networks 142: 388–396. doi:https://doi.org/10.1016/j.neunet.2021.05.026.
  • Zhao, Y., Y. Sun, W. Chen, Y. Zhao, X. Liu, and Y. Bai. 2021. “The Potential of Mapping Grassland Plant Diversity with the Links Among Spectral Diversity, Functional Trait Diversity, and Species Diversity.” Remote Sensing 13 (15): 3034. doi:10.3390/rs13153034.
  • Zhao, X., J. Zhang, Y. Huang, Y. Tian, and L. Yuan. 2022. “Detection and Discrimination of Disease and Insect Stress of Tea Plants Using Hyperspectral Imaging Combined with Wavelet Analysis.” Computers and Electronics in Agriculture 193: 10671. doi:10.1016/j.compag.2022.106717.
  • Zhong, Y., L. Wang, J. Chen, D. Yu, and Y. Li. 2020. “Comprehensive Image Captioning via Scene Graph Decomposition.” ECCV2020 1–24. http://pages.cs.wisc.edu/~yiwuzhong/Sub-GC.html.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.