參考文獻: | Anderson, P. W. (1972). More is different: Broken symmetry and the nature of the hierarchical structure of science. Science, 177(4047), 393–396. Antonijević, O., Jelić, S., Bajat, B., & Kilibarda, M. (2023). Transfer learning approach based on satellite image time series for the crop classification problem. Journal of Big Data, 10(1), 54. Avcı, M. (2000). A hierarchical classification of landsat tm imagery for land cover mapping (Unpublished master’s thesis). Middle East Technical University. Belgiu, M., & Csillik, O. (2018). Sentinel-2 cropland mapping using pixel-based and object based time-weighted dynamic time warping analysis. Remote sensing of environment, 204, 509–523. Buza, K.,&Schmidt-Thieme, L. (2010). Motif-basedclassification of time series with bayesian networks and svms. In Advances in data analysis, data handling and business intelligence: Proceedings of the 32nd annual conference of the gesellschaft für klassifikation ev, joint conference with the british classification society (bcs) and the dutch/flemish classification society (voc), helmut-schmidt-university, hamburg, july 16-18, 2008 (pp. 105–114). Cover, T. M. (1999). Elements of information theory. John Wiley & Sons. Dau, H. A., Keogh, E., Kamgar, K., Yeh, C.-C. M., Zhu, Y., Gharghabi, S., … Hexagon-ML (2018, October). The ucr time series classification archive. (https://www.cs.ucr.edu/~eamonn/time_series_data_2018/) Del Moral, P., Nowaczyk, S., Sant’Anna, A., & Pashami, S. (2023). Pitfalls of assessing extracted hierarchies for multi-class classification. Pattern Recognition, 136, 109225. Fushing, H., Chou, E. P., & Chen, T.-L. (2023). Multiscale major factor selections for complex system data with structural dependency and heterogeneity. Physica A: Statistical Mechanics and its Applications, 630, 129227. Fushing, H., Kao, H.-W., & Chou, E. P. (2024). Topological risk-landscape in metric-free categorical database. IEEE Access. Gell-Mann, M. (2002). What is complexity? In A. Q. Curzio & M. Fortis (Eds.), Complexity and industrial clusters (pp. 13–24). Heidelberg: Physica-Verlag HD. Largouët, C., & Cordier, M.-O. (2001). Improving the landcover classification using domain knowledge. AI Communications, 14(1), 35–43. Nidamanuri, R. R., & Zbell, B. (2012). Existence of characteristic spectral signatures for agri cultural crops–potential for automated crop mapping by hyperspectral imaging. Geocarto International, 27(2), 103–118. Pal, M. (2005). Randomforestclassifier for remotesensing classification. International journal of remote sensing, 26(1), 217–222. Pelletier, C., Webb, G. I., & Petitjean, F. (2019). Temporal convolutional neural network for the classification of satellite image time series. Remote Sensing, 11(5), 523. Petitjean, F., Inglada, J., & Gançarski, P. (2012). Satellite image time series analysis under time warping. IEEE transactions on geoscience and remote sensing, 50(8), 3081–3095. Radoi, A. (2022). Multimodal satellite image time series analysis using gan-based domain translation and matrix profile. Remote Sensing, 14(15), 3734. Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., & Prabhat, F. (2019). Deep learning and process understanding for data-driven earth system science. Nature, 566(7743), 195–204. Rußwurm, M., & Körner, M. (2018). Multi-temporal land cover classification with sequential recurrent encoders. ISPRS International Journal of Geo-Information, 7(4), 129. Shiu, S.-Y., Chin, Y.-S., Lin, S.-H., & Chen, T.-L. (2024). Randomized self-updating process for clustering large-scale data. Statistics and Computing, 34(1), 47. Sneath, P. H. (2005). Numerical taxonomy. In Bergey’s manual® of systematic bacteriology (pp. 39–42). Springer. Tan, C. W., Webb, G. I., & Petitjean, F. (2017). Indexing and classifying gigabytes of time series under time warping. In Proceedings of the 2017 siam international conference on data mining (pp. 282–290). Tumer, K., & Wolpert, D. H. (2004). Collectives and the design of complex systems. Springer Science & Business Media. Wu, B., Zhang, M., Zeng, H., Tian, F., Potgieter, A. B., Qin, X., … others (2023). Challenges and opportunities in remote sensing-based crop monitoring: A review. National Science Review, 10(4), nwac290. Yang, J., Gong, P., Fu, R., Zhang, M., Chen, J., Liang, S., … Dickinson, R. (2013). The role of satellite remote sensing in climate change studies. Nature climate change, 3(10), 875–883. Zhang, T., Cheng, C., & Wu, X. (2023). Mapping the spatial heterogeneity of global land use and land cover from 2020 to 2100 at a 1 km resolution. Scientific Data, 10(1), 748. Zhao, Q., Yu, L., Du, Z., Peng, D., Hao, P., Zhang, Y., & Gong, P. (2022). An overview of the applications of earth observation satellite data: impacts and future trends. Remote Sensing, 14(8), 1863. Zhu, X. X., Tuia, D., Mou, L., Xia, G.-S., Zhang, L., Xu, F., & Fraundorfer, F. (2017). Deep learning in remote sensing: A comprehensive review and list of resources. IEEE geo-science and remote sensing magazine, 5(4), 8–36. |