1 |
De Maesschalck, R., D. Jouan-Rimbaud, and D.L. Massart, 2000. The Mahalanobis distance, Chemometrics and Intelligent Laboratory Systems, 50(1): 1-18.
DOI
|
2 |
Demir, B., L. Minello, and L. Bruzzone, 2014. An effective strategy to reduce the labeling cost in the definition oftraining sets by active learning, IEEE Geoscience and Remote Sensing Letters, 11(1): 79-83.
DOI
|
3 |
Bruzzone, L., F. Roli, and S.B. Serpico, 1995. An extension of the Jeffreys-Matusita distance to multiclass cases for feature selection, IEEE Transactions on Geoscience and Remote Sensing, 33(6): 1318-1321.
DOI
|
4 |
Guo, J., H. Li, J. Ning, W. Han, W. Zhang, and Z.-S. Zhou, 2020. Feature dimension reduction using stacked sparse auto-encoders for crop classification with multi-temporal, quad-pol SAR data, Remote Sensing, 12(2): 321.
DOI
|
5 |
Hamidi, M., A. Safari, and S. Homayouni, 2020. An auto-encoder based classifierfor crop mapping from multitemporal multispectral imagery, International Journal of Remote Sensing, 42(3): 986-1016.
|
6 |
Kwak, G.-H., C.-W. Park, K.-D. Lee, S.-I. Na, H.-Y. Ahn, and N.-W. Park, 2021. Potential of hybrid CNN-RF model for early crop mapping with limited input data, Remote Sensing, 13(9): 1629.
DOI
|
7 |
Hao, P.,Y. Zhan, L.Wang, Z. Niu, and M. Shakir, 2015. Feature selection of time series MODIS data for early crop classification using random forest: A case study in Kansas, USA, Remote Sensing, 7(5): 5347-5369.
DOI
|
8 |
Hinton, G.E. and R.R. Salakhutdinov, 2006. Reducing the dimensionality of data with neural networks, Science, 313(5786): 504-507.
DOI
|
9 |
Kalinicheva, E., J. Sublime, and M. Trocan, 2020. Unsupervised satellite image time series clustering using object-based approaches and 3D convolutional autoencoder, Remote Sensing, 12(11): 1816.
DOI
|
10 |
Kwak, G.-H., M.-G. Park, C.-W. Park, K.-D. Lee, S.-I. Na, H.-Y. Ahn, and N.-W. Park, 2019. Combining 2D CNN and bidirectional LSTM to consider spatio-temporal features in crop classification, Korean Journal of Remote Sensing, 35(5-1): 681-692 (in Korean with English abstract).
DOI
|
11 |
LeCun, Y., Y. Bengio, and G. Hinton, 2015. Deep learning, Nature, 521(7553): 436-444.
DOI
|
12 |
Lee, J., B. Seo, and S. Kang, 2018. Development of a biophysical rice yield model using all-weather climate data, Korean Journal of Remote Sensing, 33(5-2): 721-732 (in Korean with English abstract).
DOI
|
13 |
Weiss, M., F. Jacob, and G. Duveiller, 2020. Remote sensing for agricultural applications: A meta-review, Remote Sensing of Environment, 236: 111402.
DOI
|
14 |
Na, S.-I., C.-W. Park, K.-H. So, H.-Y. Ahn, and K.-D. Lee, 2018. Application method of unmanned aerial vehicle for crop monitoring in Korea, Korean Journal of Remote Sensing, 34(5): 829-846 (in Korean with English abstract).
DOI
|
15 |
Li, K. and E. Xu, 2020. Cropland data fusion and correction using spatial analysis techniques and the Google Earth Engine, GIScience & Remote Sensing, 57(8): 1026-1045.
DOI
|
16 |
Hochreiter, S. and J. Schmidhuber, 1997. Long short-term memory, Neural Computation, 9(8): 1735-1780.
DOI
|
17 |
Kwak, G.-H., C.-W. Park, H.-Y. Ahn, S.-I. Na, K.-D. Lee, and N.-W. Park, 2020. Potential of bidirectional long short-term memory networks for crop classification with multitemporal remote sensing images, Korean Journal of Remote Sensing, 36(4): 515-525 (in Korean with English abstract).
DOI
|
18 |
Na, S.-I., C.-W. Park, K.-H. So, J.-M. Park, and K.-D. Lee, 2017. Satellite imagery based winter crop classification mapping using hierarchical classification, Korean Journal of Remote Sensing, 33(5-2): 677-687 (in Korean with English abstract).
DOI
|
19 |
Russwurm, M. and M. Korner, 2018. Multi-temporal land cover classification with sequential recurrent encoders, ISPRS International Journal of Geo-Information, 7(4): 129.
DOI
|
20 |
Shi, X., Z. Chen, H. Wang, D.-Y. Yeung, W.-K. Wong, and W.-C. Woo, 2015. Convolutional LSTM network: A machine learning approach for precipitation nowcasting, Proc. of the Advances in Neural Information Processing Systems, Montreal, QC, CA, Dec. 7-12, pp. 802-810.
|
21 |
Zhong, L., L. Hu, and H. Zhou, 2019. Deep learning based multi-temporal crop classification, Remote Sensing of Environment, 221: 430-443.
DOI
|
22 |
Zhou, Y., J. Luo, L. Feng, Y. Yang, Y. Chen, and W. Wu, 2019. Long-short-term-memory-based crop classification using high-resolution optical images and multi-temporal SAR data, GIScience & Remote Sensing, 56(8): 1170-1191.
DOI
|