1 곽근호, "작물 분류에서 시공간 특징을 고려하기 위한 2D CNN과 양방향 LSTM의 결합" 대한원격탐사학회 35 (35): 681-692, 2019
2 나상일, "국내 작황 모니터링을 위한 무인항공기 적용방안" 대한원격탐사학회 34 (34): 829-846, 2018
3 나상일, "계층분류 기법을 이용한 위성영상 기반의 동계작물 구분도 작성" 대한원격탐사학회 33 (33): 677-687, 2017
4 Kalinicheva, E., "Unsupervised satellite image time series clustering using object-based approaches and 3D convolutional autoencoder" 12 (12): 1816-, 2020
5 De Maesschalck, R., "The Mahalanobis distance" 50 (50): 1-18, 2000
6 Weiss, M., "Remote sensing for agricultural applications : A metareview" 236 : 111402-, 2020
7 Hinton, G. E., "Reducing the dimensionality of data with neural networks" 313 (313): 504-507, 2006
8 Kwak, G. -H., "Potential of hybrid CNN-RF model for early crop mapping with limited input data" 13 (13): 1629-, 2021
9 Geun-Ho Kwak, "Potential of Bidirectional Long Short-Term Memory Networks for Crop Classification with Multitemporal Remote Sensing Images" 대한원격탐사학회 36 (36): 515-525, 2020
10 Rußwurm, M., "Multi-temporal land cover classification with sequential recurrent encoders" 7 (7): 129-, 2018
1 곽근호, "작물 분류에서 시공간 특징을 고려하기 위한 2D CNN과 양방향 LSTM의 결합" 대한원격탐사학회 35 (35): 681-692, 2019
2 나상일, "국내 작황 모니터링을 위한 무인항공기 적용방안" 대한원격탐사학회 34 (34): 829-846, 2018
3 나상일, "계층분류 기법을 이용한 위성영상 기반의 동계작물 구분도 작성" 대한원격탐사학회 33 (33): 677-687, 2017
4 Kalinicheva, E., "Unsupervised satellite image time series clustering using object-based approaches and 3D convolutional autoencoder" 12 (12): 1816-, 2020
5 De Maesschalck, R., "The Mahalanobis distance" 50 (50): 1-18, 2000
6 Weiss, M., "Remote sensing for agricultural applications : A metareview" 236 : 111402-, 2020
7 Hinton, G. E., "Reducing the dimensionality of data with neural networks" 313 (313): 504-507, 2006
8 Kwak, G. -H., "Potential of hybrid CNN-RF model for early crop mapping with limited input data" 13 (13): 1629-, 2021
9 Geun-Ho Kwak, "Potential of Bidirectional Long Short-Term Memory Networks for Crop Classification with Multitemporal Remote Sensing Images" 대한원격탐사학회 36 (36): 515-525, 2020
10 Rußwurm, M., "Multi-temporal land cover classification with sequential recurrent encoders" 7 (7): 129-, 2018
11 이지혜, "MODIS 전천후 기상자료 기반의 생물리학적 벼 수량 모형 개발" 대한원격탐사학회 33 (33): 721-732, 2017
12 Zhou, Y., "Long-short-term-memory-based crop classification using high-resolution optical images and multi-temporal SAR data" 56 (56): 1170-1191, 2019
13 Hochreiter, S., "Long shortterm memory" 9 (9): 1735-1780, 1997
14 Hao, P., "Feature selection of time series MODIS data for early crop classification using random forest : A case study in Kansas, USA" 7 (7): 5347-5369, 2015
15 Guo, J., "Feature dimension reduction using stacked sparse auto-encoders for crop classification with multi-temporal, quad-pol SAR data" 12 (12): 321-, 2020
16 Zhong, L., "Deep learning based multi-temporal crop classification" 221 : 430-443, 2019
17 LeCun, Y., "Deep learning" 521 (521): 436-444, 2015
18 Li, K., "Cropland data fusion and correction using spatial analysis techniques and the Google Earth Engine" 57 (57): 1026-1045, 2020
19 Shi, X., "Convolutional LSTM network: A machine learning approach for precipitation nowcasting" 802-810, 2015
20 Bruzzone, L., "An extension of the Jeffreys-Matusita distance to multiclass cases for feature selection" 33 (33): 1318-1321, 1995
21 Demir, B., "An effective strategy to reduce the labeling cost in the definition of training sets by active learning" 11 (11): 79-83, 2014
22 Hamidi, M., "An auto-encoder based classifier for crop mapping from multitemporal multispectral imagery" 42 (42): 986-1016, 2020