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GIScience & Remote Sensing Volume 60, 2023 - Issue 1
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Research Article

Histogram matching-based semantic segmentation model for crop classification with Sentinel-2 satellite imagery

Lijun Wanga Henan Industrial Technology Academy of Spatio-Temporal Big Data, Henan University, Kaifeng, China;b College of Geography and Environmental Science, Henan University, Kaifeng, China;c Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Henan University, Kaifeng, ChinaView further author information
,
Yang Baia Henan Industrial Technology Academy of Spatio-Temporal Big Data, Henan University, Kaifeng, China;b College of Geography and Environmental Science, Henan University, Kaifeng, China;c Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Henan University, Kaifeng, ChinaCorrespondence[email protected]
View further author information
,
Jiayao Wanga Henan Industrial Technology Academy of Spatio-Temporal Big Data, Henan University, Kaifeng, China;b College of Geography and Environmental Science, Henan University, Kaifeng, China;d Henan Technology Innovation Center of Spatial-Temporal Big Data, Henan University, Kaifeng, ChinaView further author information
,
Zheng Zhoua Henan Industrial Technology Academy of Spatio-Temporal Big Data, Henan University, Kaifeng, China;b College of Geography and Environmental Science, Henan University, Kaifeng, China;c Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Henan University, Kaifeng, ChinaView further author information
,
Fen Qina Henan Industrial Technology Academy of Spatio-Temporal Big Data, Henan University, Kaifeng, China;b College of Geography and Environmental Science, Henan University, Kaifeng, China;d Henan Technology Innovation Center of Spatial-Temporal Big Data, Henan University, Kaifeng, China;e Key Research Institute of Yellow River Civilization and Sustainable Development, Ministry of Education, Henan University, Kaifeng, ChinaView further author information
&
Jiyuan Hua Henan Industrial Technology Academy of Spatio-Temporal Big Data, Henan University, Kaifeng, China;b College of Geography and Environmental Science, Henan University, Kaifeng, China;c Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Henan University, Kaifeng, ChinaView further author information
Article: 2281142 | Received 28 Apr 2023, Accepted 03 Nov 2023, Published online: 16 Nov 2023

References

  • Adrian, J., V. Sagan, and M. Maimaitijiang. 2021. “Sentinel SAR-Optical Fusion for Crop Type Mapping Using Deep Learning and Google Earth Engine.” Isprs Journal of Photogrammetry & Remote Sensing 175:215–21. https://doi.org/10.1016/j.isprsjprs.2021.02.018.
  • Badrinarayanan, V., A. Handa, and R. Cipolla. 2015. “SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labelling.” Computer Science arXiv:1505.07293.https://doi.org/10.48550/arXiv.1505.07293.
  • Cao, B. A., Y. M. Du, D. Q. Xu, H. Li, and Q. H. Liu. 2015. “An Improved Histogram Matching Algorithm for the Removal of Striping Noise in Optical Remote Sensing Imagery.” Optik 126 (23): 4723–4730. https://doi.org/10.1016/j.ijleo.2015.08.079.
  • Chaves, M. E. D., M. C. A. Picoli, and I. D. Sanches. 2020. “Recent Applications of Landsat 8/OLI and Sentinel-2/MSI for Land Use and Land Cover Mapping: A Systematic Review.” Remote Sensing 12 (18): 3062. https://doi.org/10.3390/rs12183062.
  • Chen, L. C., Y. K. Zhu, G. Papandreou, F. Schroff, and H. Adam. 2018. “Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation.” Proceedings of the European conference on computer vision (ECCV) 11211: 833–851. https://doi.org/10.48550/arXiv.1802.02611.
  • Cui, H., L. Zhang, H. B. Ai, B. Xu, and Z. H. Wang. 2017. “Large Collection Satellite Images Color Normalization Algorithm Based on Tone Reference Map.” Acta Geodaetica et Cartographica Sinica 46 (12): 1986–1997. https://doi.org/10.11947/j.AGCS.2017.20170402.
  • Diakogiannis, F. I., F. Waldner, P. Caccetta, and C. Wu. 2020. “ResUnet-A: A Deep Learning Framework for Semantic Segmentation of Remotely Sensed Data.” Isprs Journal of Photogrammetry & Remote Sensing 162: 94–114. https://doi.org/10.1016/j.isprsjprs.2020.01.013.
  • Hao, P. Y., L. P. Di, C. Zhang, and L. Y. Guo. 2020. “Transfer Learning for Crop Classification with Cropland Data Layer Data (CDL) as Training Samples.” Science of the Total Environment 733: 138869. https://doi.org/10.1016/j.scitotenv.2020.138869.
  • Helmer, E. H., and B. Ruefenacht. 2006. “Cloud-Free Satellite Image Mosaics with Regression Trees and Histogram Matching.” Photogrammetric Engineering & Remote Sensing 71 (9): 1079–1089. https://doi.org/10.14358/PERS.71.9.1079.
  • Horn, K. P. B., and R. J. Woodham. 1979. “Destriping LANDSAT MSS Images by Histogram Modification.” Computer Graphics and Image Processing 10 (1): 69–83. https://doi.org/10.1016/0146-664X(79)90035-2.
  • Ienco, D., R. Interdonato, R. Gaetano, and D. H. T. Minh. 2019. “Combining Sentinel-1 and Sentinel-2 Satellite Image Time Series for Land Cover Mapping via a Multi-Source Deep Learning Architecture.” Isprs Journal of Photogrammetry & Remote Sensing 158: 11–22. https://doi.org/10.1016/j.isprsjprs.2019.09.016.
  • Izquierdo-Verdiguier, E., and R. Zurita-Milla. 2020. “An Evaluation of Guided Regularized Random Forest for Classification and Regression Tasks in Remote Sensing.” International Journal of Applied Earth Observation and Geoinformation 88: 102051. https://doi.org/10.1016/j.jag.2020.102051.
  • Jayasankari, S., and S. Domnic. 2020. “Histogram Shape Based Gaussian Sub-Histogram Specification for Contrast Enhancement.” Intelligent Decision Technologies-Netherlands 14 (1): 67–80. https://doi.org/10.3233/IDT-190081.
  • Kaiser, P., J. D. Wegner, A. Lucchi, M. Jaggi, T. Hofmann, and K. Schindler. 2017. “Learning Aerial Image Segmentation from Online Maps.” IEEE Transactions on Geoscience & Remote Sensing 55 (11): 6054–6068. https://doi.org/10.1109/TGRS.2017.2719738.
  • Khan, A. H., M. M. Fraz, and M. Shahzad. 2021. “Deep Learning Based Land Cover and Crop Type Classification: A Comparative Study.” 2021 International Conference on Digital Futures and Transformative Technologies (Icodt2), Islamabad, Pakistan, 1–6. https://doi.org/10.1109/ICoDT252288.2021.9441483.
  • Li, Y. S., W. Chen, Y. J. Zhang, C. Tao, R. Xiao, and Y. H. Tan. 2020. “Accurate Cloud Detection in High-Resolution Remote Sensing Imagery by Weakly Supervised Deep Learning.” Remote Sensing of Environment 250: 112045. https://doi.org/10.1016/j.rse.2020.112045.
  • Li, W. B., K. M. Sun, Z. T. Du, X. Q. Hu, W. Z. Li, J. J. Wei, and S. Gao. 2021. “PCNet: Cloud Detection in FY-3D True-Color Imagery Using Multi-Scale Pyramid Contextual Information.” Remote Sensing 13 (18): 3670. https://doi.org/10.3390/rs13183670.
  • Li, Z. H., H. Y. Zhang, F. X. Lu, R. Y. Xue, G. Y. Yang, and L. P. Zhang. 2022. “Breaking the Resolution Barrier: A Low-To-High Network for Large-Scale High-Resolution Land-Cover Mapping Using Low-Resolution Labels.” Isprs Journal of Photogrammetry & Remote Sensing 192: 244–267. https://doi.org/10.1016/j.isprsjprs.2022.08.008.
  • Martinez, J. A. C., L. E. C. La Rosa, R. Q. Feitosa, I. D. Sanches, and P. N. Happ. 2021. “Fully Convolutional Recurrent Networks for Multidate Crop Recognition from Multitemporal Image Sequences.” Isprs Journal of Photogrammetry & Remote Sensing 171: 188–201. https://doi.org/10.1016/j.isprsjprs.2020.11.007.
  • Paris, C., and L. Bruzzone. 2019. “Automatic Extraction of Weak Labeled Samples from Existing Thematic Products for Training Convolutional Neural Networks.” 2019 Ieee International Geoscience and Remote Sensing Symposium (Igarss 2019), Yokohama, Japan, 5722–5725. https://doi.org/10.1109/IGARSS.2019.8900649.
  • Paris, C., and L. Bruzzone. 2021. “A Novel Approach to the Unsupervised Extraction of Reliable Training Samples from Thematic Products.” IEEE Transactions on Geoscience & Remote Sensing 59 (3): 1930–1948. https://doi.org/10.1109/TGRS.2020.3001004.
  • Perantoni, G., and L. Bruzzone. 2022. “A Novel Technique for Robust Training of Deep Networks with Multisource Weak Labeled Remote Sensing Data.” IEEE Transactions on Geoscience & Remote Sensing 60: 1–15. https://doi.org/10.1109/TGRS.2021.3091482.
  • Portales-Julia, E., M. Campos-Taberner, F. J. Garcia-Haro, and M. A. Gilabert. 2021. “Assessing the Sentinel-2 Capabilities to Identify Abandoned Crops Using Deep Learning.” Agronomy-Basel 11 (4): 654. https://doi.org/10.3390/agronomy11040654.
  • Qi, X. Q., K. Q. Li, P. K. Liu, X. G. Zhou, and M. Y. Sun. 2020. “Deep Attention and Multi-Scale Networks for Accurate Remote Sensing Image Segmentation.” Institute of Electrical and Electronics Engineers Access 8: 146627–146639. https://doi.org/10.1109/ACCESS.2020.3015587.
  • Ronneberger, O., P. Fischer, and T. Brox. 2015. “U-Net: Convolutional Networks for Biomedical Image Segmentation.” Medical Image Computing and Computer-Assisted Intervention, Pt Iii 9351: 234–241. https://doi.org/10.1007/978-3-319-24574-4_28.
  • Roy, A. G., N. Navab, and C. Wachinger. 2018. “Concurrent Spatial and Channel ‘Squeeze & Excitation’ in Fully Convolutional Networks.” Medical Image Computing and Computer Assisted Intervention - Miccai 2018, Pt I 11070: 421–429. https://doi.org/10.1007/978-3-030-00928-1_48.
  • Song, X. P., W. L. Huang, M. C. Hansen, and P. Potapov. 2021. “An Evaluation of Landsat, Sentinel-2, Sentinel-1 and MODIS Data for Crop Type Mapping.” Science of Remote Sensing 3: 100018. https://doi.org/10.1016/j.srs.2021.100018.
  • Su, N. E., S. Jarvie, Y. Z. Yan, X. Q. Gong, F. S. Li, P. Han, and Q. Zhang. 2022. “Landscape context determines soil fungal diversity in a fragmented habitat.” Catena 213: 106163. https://doi.org/10.1016/j.catena.2022.106163.
  • Waldner, F., and F. I. Diakogiannis. 2020. “Deep Learning on Edge: Extracting Field Boundaries from Satellite Images with a Convolutional Neural Network.” Remote Sensing of Environment 245: 111741. https://doi.org/10.1016/j.rse.2020.111741.
  • Wang, Y. M., L. W. Feng, W. W. Sun, Z. Zhang, H. Y. Zhang, G. Yang, and X. C. Meng. 2022. “Exploring the Potential of Multi-Source Unsupervised Domain Adaptation in Crop Mapping Using Sentinel-2 Images.” GIScience & Remote Sensing 59 (1):2247–2265. https://doi.org/10.1080/15481603.2022.2156123.
  • Wang, L. J., J. Y. Wang, Z. Z. Liu, J. Zhu, and F. Qin. 2022b. “Evaluation of a Deep-Learning Model for Multispectral Remote Sensing of Land Use and Crop Classification.” The Crop Journal 10 (5): 1435–1451. https://doi.org/10.1016/j.cj.2022.01.009.
  • Wang, L. J., J. Y. Wang, X. W. Zhang, L. G. Wang, and F. Qin. 2022a. “Deep Segmentation and Classification of Complex Crops Using Multi-Feature Satellite Imagery.” Computers and Electronics in Agriculture 200: 107249. https://doi.org/10.1016/j.compag.2022.107249.
  • Wang, Y. M., Z. Zhang, L. W. Feng, Y. C. Ma, and Q. Y. Du. 2021. “A New Attention-Based CNN Approach for Crop Mapping Using Time Series Sentinel-2 Images.” Computers and Electronics in Agriculture 184: 106090. https://doi.org/10.1016/j.compag.2021.106090.
  • Xu, Q., J. S. Zhang, F. Zhang, S. Ge, Z. Yang, and Y. M. Duan. 2022. “Applicability of Weak Samples to Deep Learning Crop Classification.” National Remote Sensing Bulletin 26 (7): 1395–1409. https://doi.org/10.11834/jrs.20221127.
  • Xu, J. F., Y. Zhu, R. H. Zhong, Z. X. Lin, J. L. Xu, H. Jiang, J. F. Huang, H. F. Li, and T. Lin. 2020. “DeepCropmapping: A Multi-Temporal Deep Learning Approach with Improved Spatial Generalizability for Dynamic Corn and Soybean Mapping.” Remote Sensing of Environment 247: 111946. https://doi.org/10.1016/j.rse.2020.111946.
  • Yuan, X. H., J. F. Shi, and L. C. Gu. 2021. “A Review of Deep Learning Methods for Semantic Segmentation of Remote Sensing Imagery.” Expert Systems with Applications 169: 114417. https://doi.org/10.1016/j.eswa.2020.114417.
  • Zhang, J. Z., S. J. Cong, G. Zhang, Y. J. Ma, Y. Zhang, and J. P. Huang. 2022. “Detecting Pest-Infested Forest Damage Through Multispectral Satellite Imagery and Improved UNet Plus.” Sensors 22 (19): 7440. https://doi.org/10.3390/s22197440.
  • Zhou, Z. W., M. M. R. Siddiquee, N. Tajbakhsh, and J. M. Liang. 2018. “UNet Plus Plus: A Nested U-Net Architecture for Medical Image Segmentation.” Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Dlmia 2018 11045: 3–11. https://doi.org/10.1007/978-3-030-00889-5_1.
  • Zhu, Q. Q., Y. Lei, X. L. Sun, Q. F. Guan, Y. F. Zhong, L. P. Zhang, and D. R. Li. 2022. “Knowledge-Guided Land Pattern Depiction for Urban Land Use Mapping: A Case Study of Chinese Cities.” Remote Sensing of Environment 272: 112916. https://doi.org/10.1016/j.rse.2022.112916.
  • Zhu, X. X., D. Tuia, L. C. Mou, G. S. Xia, L. P. Zhang, F. Xu, and F. Fraundorfer. 2017. “Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources.” IEEE Geoscience and Remote Sensing Magazine 5 (4): 8–36. https://doi.org/10.1109/MGRS.2017.2762307.

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