Modified HDBSCAN based segmentation hyperspectral image segmentation for cotton crop classification

References

• Adrian, J., Sagan, V., and Maimaitijiang, M., 2021. Sentinel SAR-optical fusion for crop type mapping using deep learning and Google earth engine. ISPRS Journal of Photogrammetry and Remote Sensing, 175, 215–235. doi:10.1016/j.isprsjprs.2021.02.018
   Web of Science ®Google Scholar
• Beriaux, E., et al., 2021. Sentinel-1 time series for crop identification in the framework of the future CAP monitoring. Remote Sensing, 13 (14), 2785. doi:10.3390/rs13142785.
   Web of Science ®Google Scholar
• Bhuyar, N., Acharya, S., and Theng, D., 2020. Crop classification with multi-temporal satellite image data. International Journal of Engineering Research, 9 (6). doi:10.17577/IJERTV9IS060208.
   Google Scholar
• Boiarskii, B., 2020. Satellite high-spatial-resolution multispectral imagery for crop type identification using sentinel application platform and R software. Journal of Physics Conference Series, 1679 (3), 032046. IOP Publishing: doi:10.1088/1742-6596/1679/3/032046.
   Google Scholar
• Cheng, T., et al., 2020. DESTIN: a new method for delineating the boundaries of crop fields by fusing spatial and temporal information from WorldView and planet satellite imagery. Computers and Electronics in Agriculture, 178, 105787. doi:10.1016/j.compag.2020.105787
   Web of Science ®Google Scholar
• Fernández-Sellers, M., et al., 2022. Finding a suitable sensing time period for crop identification using heuristic techniques with multi-temporal satellite images. International Journal of Remote Sensing, 43 (15–16), 6038–6055. doi:10.1080/01431161.2021.1975846.
   Web of Science ®Google Scholar
• Gadiraju, K.K., et al., 2020. Multimodal deep learning based crop classification using multispectral and multitemporal satellite imagery. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, USA, 3234–3242.
   Google Scholar
• Gao, H., et al., 2021. A novel crop classification method based on ppfSVM classifier with time-series alignment kernel from dual-polarization SAR datasets. Remote Sensing of Environment, 264, 112628. doi:10.1016/j.rse.2021.112628
   Web of Science ®Google Scholar
• Gumma, M.K., et al., 2022. Crop type identification and spatial mapping using sentinel-2 satellite data with focus on field-level information. Geocarto International, 37 (7), 1833–1849. doi:10.1080/10106049.2020.1805029.
   Web of Science ®Google Scholar
• Guo, Z., et al., 2022. Identification of crop type based on C-AENN using time series sentinel-1A SAR data. Remote Sensing, 14 (6), 1379.
   Web of Science ®Google Scholar
• https://bhuvan-app3.nrsc.gov.in/data/download/index.php?c=p&s=NI&p=acd&g=TS#
   Google Scholar
• https://en.wikipedia.org/wiki/Standard_deviation
   Google Scholar
• https://en.wikipedia.org/wiki/Statistic.
   Google Scholar
• Kaushal, C., et al., 2022. A framework for interactive medical image segmentation using optimized swarm intelligence with convolutional neural networks. Computational Intelligence and Neuroscience, 2022. doi:10.1155/2022/7935346.
   PubMed Web of Science ®Google Scholar
• LeCun, Y., Kavukcuoglu, K., and Farabet, C., 2010. Convolutional networks and applications in vision. In Proceedings of 2010 IEEE international symposium on circuits and systems, Paris, France. IEEE, 253–256.
   Google Scholar
• Lei, L., et al., 2021. DOCC: deep one-class crop classification via positive and unlabeled learning for multi-modal satellite imagery. International Journal of Applied Earth Observation and Geoinformation, 105, 102598. doi:10.1016/j.jag.2021.102598
   Web of Science ®Google Scholar
• Li, H., et al., 2022. Temporal sequence object-based CNN (TS-OCNN) for crop classification from fine resolution remote sensing image time-series. The Crop Journal, 10 (5), 1507–1516. doi:10.1016/j.cj.2022.07.005.
   Google Scholar
• Lozano-Tello, A., et al., 2021. Crop identification by massive processing of multiannual satellite imagery for EU common agriculture policy subsidy control. European Journal of Remote Sensing, 54 (1), 1–12. doi:10.1080/22797254.2020.1858723.
   Web of Science ®Google Scholar
• Oh, M.S., et al., 2020. An economic feasibility assessment framework for underutilised crops using support vector machine. Computers and Electronics in Agriculture, 168, 105116. doi:10.1016/j.compag.2019.105116
   Web of Science ®Google Scholar
• Shah, G.H., 2012. An improved DBSCAN, a density based clustering algorithm with parameter selection for high dimensional data sets. In 2012 Nirma university international conference on engineering (NUiCONE), Ahmedabad, India. IEEE, 1–6.
   Google Scholar
• Shelestov, A., et al., 2019. Cloud approach to automated crop classification using sentinel-1 imagery. IEEE Transactions on Big Data, 6 (3), 572–582. doi:10.1109/TBDATA.2019.2940237.
   Web of Science ®Google Scholar
• Shukla, P.K., et al., 2021. Multiobjective genetic algorithm and convolutional neural network based COVID-19 identification in chest X-ray images. Mathematical Problems in Engineering, 2021, 1–9. doi:10.1155/2021/7804540
   Web of Science ®Google Scholar
• Siesto, G., Fernández-Sellers, M., and Lozano-Tello, A., 2021. Crop classification of satellite imagery using synthetic multitemporal and multispectral images in convolutional neural networks. Remote Sensing, 13 (17), 3378. doi:10.3390/rs13173378.
   Web of Science ®Google Scholar
• Singh, H., Kaur, S., and Kaushal, C., 2022. Text document clustering using partial fractionation and bisecting K-means. In 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO), Noida, India. IEEE, 1–6.
   Google Scholar
• Sood, V., et al., 2018. Spatial and quantitative comparison of topographically derived different classification algorithms using AWiFS data over Himalayas, India. Journal of the Indian Society of Remote Sensing, 46 (12), 1991–2002. doi:10.1007/s12524-018-0861-4.
   Web of Science ®Google Scholar
• Sood, V., et al., 2020. Monitoring and mapping of snow cover variability using topographically derived NDSI model over north Indian Himalayas during the period 2008–19. Applied Computing and Geosciences, 8, 100040. doi:10.1016/j.acags.2020.100040
   Google Scholar
• Tian, Y., et al., 2021. Machine learning-based crop recognition from aerial remote sensing imagery. Frontiers of Earth Science, 15 (1), 54–69. doi:10.1007/s11707-020-0861-x.
   Web of Science ®Google Scholar
• Trivedi, N.K., et al., 2021. Early detection and classification of tomato leaf disease using high-performance deep neural network. Sensors, 21 (23), 7987. doi:10.3390/s21237987.
   PubMed Web of Science ®Google Scholar
• Verma, A.K., Garg, P.K., and Hari Prasad, K.S., 2017. Sugarcane crop identification from LISS IV data using ISODATA, MLC, and indices based decision tree approach. Arabian Journal of Geosciences, 10 (1), 1–17. doi:10.1007/s12517-016-2815-x.
   Web of Science ®Google Scholar
• Wang, H.Z., et al., 2016. Deep belief network based deterministic and probabilistic wind speed forecasting approach. Applied Energy, 182, 80–93. doi:10.1016/j.apenergy.2016.08.108
   Web of Science ®Google Scholar
• Woźniak, E., et al., 2022. Multi-temporal phenological indices derived from time series sentinel-1 images to country-wide crop classification. International Journal of Applied Earth Observation and Geoinformation, 107, 102683. doi:10.1016/j.jag.2022.102683
   Web of Science ®Google Scholar
• Yan, S., et al., 2021. Large-scale crop mapping from multi-source optical satellite imageries using machine learning with discrete grids. International Journal of Applied Earth Observation and Geoinformation, 103, 102485. doi:10.1016/j.jag.2021.102485
   Web of Science ®Google Scholar
• Zhong, Y., et al., 2020. WHU-Hi: UAV-borne hyperspectral with high spatial resolution (H2) benchmark datasets and classifier for precise crop identification based on deep convolutional neural network with CRF. Remote Sensing of Environment, 250, 112012. doi:10.1016/j.rse.2020.112012
   Web of Science ®Google Scholar
• Zhou, X., et al., 2020. Improved itracker combined with bidirectional long short-term memory for 3D gaze estimation using appearance cues. Neurocomputing, 390, 217–225. doi:10.1016/j.neucom.2019.04.099
   Web of Science ®Google Scholar