Open Access
Issue
E3S Web Conf.
Volume 600, 2024
The 6th International Geography Seminar (IGEOS 2023)
Article Number 03001
Number of page(s) 7
Section GIS and Remote Sensing Application
DOI https://doi.org/10.1051/e3sconf/202460003001
Published online 29 November 2024
  1. S. Ghosh, S. Patra, and A. Ghosh, An unsupervised context-sensitive change detection technique based on modified self-organizing feature map neural network. Int. J. Approx. Reason., vol. 50, pp. 37–50, (2009). [CrossRef] [Google Scholar]
  2. D. Lu, P. Mausel, E. Brondizio, and E. Moran, Change detection techniques. Int. J. Remote Sens., vol. 25, pp. 2365–2401. (2004). [CrossRef] [Google Scholar]
  3. A. Singh, Review article digital change detection techniques using remotely-sensed data. Int. J. Remote Sens., vol. 10, pp. 989–1003, 1989. [CrossRef] [Google Scholar]
  4. A. H. Chughtai, H. Abbasi, and R. K. Ismail, A review on change detection method and accuracy assessment for land use land cover. Remote Sens. Appl.: Soc. Environ., vol. 100482, 2021. [Google Scholar]
  5. S. Liu, D. Marinelli, L. Bruzzone, and F. Bovolo, A review of change detection in multitemporal hyperspectral images: Current techniques, applications, and challenges”, IEEE Geosci. Remote Sens. Mag., vol. 7, pp. 140–158. (2019). [CrossRef] [Google Scholar]
  6. S. T. Seydi and M. Hasanlou, A new land-cover match-based change detection for hyperspectral imagery. Eur. J. Remote Sens., vol. 50, pp. 517–533, 2017. [CrossRef] [Google Scholar]
  7. D. Guo, W. Shi, M. Hao, and X. Zhu, FSDAF 2.0: Improving the performance of retrieving land cover changes and preserving spatial details. Remote Sens. Environ., vol. 248, pp. 111973, (2020). [CrossRef] [Google Scholar]
  8. C. Wu, B. Du, and L. Zhang, Hyperspectral anomalous change detection based on joint sparse representation. ISPRS J. Photogramm. Remote Sens., vol. 146, pp. 137–150, (2018). [CrossRef] [Google Scholar]
  9. M. Dalponte, L. Bruzzone, L. Vescovo, and D. Gianelle, The role of spectral resolution and classifier complexity in the analysis of hyperspectral images of forest areas. Remote Sens. Environ., vol. 113, pp. 2345–2355. (2009). [CrossRef] [Google Scholar]
  10. S. Pignatti, A. Palombo, S. Pascucci, F. Romano, F. Santini, T. Simoniello, A. Umberto, C. Vincenzo, N. Acito, and M. Diani, The PRISMA hyperspectral mission: Science activities and opportunities for agriculture and land monitoring, in Proc. IEEE Int. Geosci. Remote Sens. Symp. (IGARSS), pp. 4558–4561. (2013) [Google Scholar]
  11. E. Lopinto and C. Ananasso, The Prisma hyperspectral mission. in Proc. 33rd EARSeL Symp. Towards Horizon. (2020). [Google Scholar]
  12. M. Kamal and S. Arjasakusuma, “Ekstraksi Informasi Penutup Lahan Menggunakan Spektrometer Lapangan Sebagai Masukan Endmember pada Data Hiperspektral Resolusi Sedang”, Jurnal Ilmiah Geomatika, vol. 16. (2010). [Google Scholar]
  13. M. Hasanlou and S. T. Seydi, “Hyperspectral change detection: An experimental comparative study”, Int. J. Remote Sens., vol. 39, pp. 7029–7083. (2018). [CrossRef] [Google Scholar]
  14. T. Celik, “Unsupervised change detection in satellite images using principal component analysis and kmeans clustering”, IEEE Geosci. Remote Sens. Lett., vol. 6, pp. 772–776. (2009). [CrossRef] [Google Scholar]
  15. S. Arjasakusuma, P. Danoedoro, S. Herumurti, Y. A. Nugroho, and P. A. Aryaguna, “Land-Soil Characteristics For Mapping Paddy Cropping Intensity Using Decision Tree Analysis from Single Date ALI Imagery in Magelang, Central Java, Indonesia”, Geoplanning: J. Geomatics Plan., vol. 4, pp. 187–200. (2017). [CrossRef] [Google Scholar]
  16. S. Arjasakusuma, S. Kusuma, W. Mahendra, and N. Astriviany, “Mapping Paddy Field Extent and Temporal Pattern Variation in a Complex Terrain Area using Sentinel 1-Time Series Data: Case Study of Magelang District, Indonesia”, Int. J. Geoinformatics, vol. 17, 2021. [Google Scholar]
  17. B. Leutner, N. Horning, and M. B. Leutner, “Package ‘RStoolbox’”, R Foundation for Statistical Computing, Version 0.1, 2017. [Google Scholar]
  18. S. Hantson and E. Chuvieco, “Evaluation of different topographic correction methods for Landsat imagery”, Int. J. Appl. Earth Obs. Geoinformation, vol. 13, pp. 691–700, 2011. [CrossRef] [Google Scholar]
  19. U. Ligges, T. Short, P. Kienzle, S. Schnackenberg, D. Billinghurst, H.-W. Borchers, A. Carezia, P. Dupuis, J. W. Eaton, and E. Farhi, “Package ‘signal’”, R Foundation for Statistical Computing, 2015. [Google Scholar]
  20. L. W. Lehnert, H. Meyer, W. A. Obermeier, B. Silva, B. Regeling, and J. Bendix, “Hyperspectral data analysis in R: The hsdar package”, arXiv preprint arXiv:1805.05090, 2018. [Google Scholar]
  21. S. Arjasakusuma, S. Swahyu Kusuma, and S. Phinn, “Evaluating variable selection and machine learning algorithms for estimating forest heights by combining lidar and hyperspectral data”, ISPRS Int. J. Geo-Inf., vol. 9, pp. 507, 2020. [CrossRef] [Google Scholar]

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