- 1.
Acción Á, Argüello F and Heras D B. 2020. Dual-window superpixel data augmentation for hyperspectral image classification. Applied Sciences, 10(24): 8833
- 2.
Belgiu M and Drăguţ L. 2016. Random forest in remote sensing: a review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114: 24-31
- 3.
Chen H J, Lyu D N, Zhou X and Liu J. 2024. Cross-domain transfer learning algorithm for few-shot ship recognition in remote-sensing images. National Remote Sensing Bulletin, 28(3): 793-804
- 4.
Chen X, Ma J W and Dai Q. 2005. Remote sensing change detection based on bayesian networks classifications. Journal of Remote Sensing (in Chinese), 9(6): 667-672
- 5.
Chua L O. 1997. CNN: a vision of complexity. International Journal of Bifurcation and Chaos, 7(10): 2219-2425
- 6.
Danielsson P E. 1980. Euclidean distance mapping. Computer Graphics and Image Processing, 14(3): 227-248
- 7.
Fan X N, Yan W, Shi P F and Zhang X W. 2022. Remote sensing image target detection based on a multi-scale deep feature fusion network. National Remote Sensing Bulletin, 26(11): 2292-2303
- 8.
Fekri E, Latifi H, Amani M and Zobeidinezhad A. 2021. A training sample migration method for wetland mapping and monitoring using sentinel data in google earth engine. Remote Sensing, 13(20): 4169
- 9.
Ghorbanian A, Kakooei M, Amani M, Mahdavi S, Mohammadzadeh A and Hasanlou M. 2020. Improved land cover map of Iran using Sentinel imagery within Google Earth Engine and a novel automatic workflow for land cover classification using migrated training samples. ISPRS Journal of Photogrammetry and Remote Sensing, 167: 276-288
- 10.
Glanz H and Carvalho L. 2018. An expectation-maximization algorithm for the matrix normal distribution with an application in remote sensing. Journal of Multivariate Analysis, 167: 31-48
- 11.
Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A and Bengio Y. 2020. Generative adversarial networks. Communications of the ACM, 63(11): 139-144
- 12.
Grabska E, Hostert P, Pflugmacher D and Ostapowicz K. 2019. Forest stand species mapping using the Sentinel-2 time series. Remote Sensing, 11(10): 1197
- 13.
Guo G D, Wang H, Bell D, Bi Y X and Greer K. 2003. KNN model-based approach in classification//Proceedings of OTM Confederated International Conferences CoopIS, DOA, and ODBASE 2003 on the Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE. Sicily: IEEE: 986-996
- 14.
Hong D F, Yokoya N, Ge N, Chanussot J and Zhu X X. 2019. Learnable manifold alignment (LeMA): a semi-supervised cross-modality learning framework for land cover and land use classification. ISPRS Journal of Photogrammetry and Remote Sensing, 147: 193-205
- 15.
Huang H B, Chen Y L, Clinton N, Wang J, Wang X Y, Liu C X, Gong P, Yang J, Bai Y Q, Zheng Y M and Zhu Z L. 2017. Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine. Remote Sensing of Environment, 202: 166-176
- 16.
Huang H B, Wang J, Liu C X, Liang L, Li C C and Gong P. 2020. The migration of training samples towards dynamic global land cover mapping. ISPRS Journal of Photogrammetry and Remote Sensing, 161: 27-36
- 17.
Li J, Li Z W, Ding X L, Zhu J and Wang C C. 2021. Filtering strong noisy synthetic aperture radar (SAR) interferogram with integrated Contoured Median and Goldstein two-step filter. Journal of Remote Sensing (in Chinese), 15(4): 750-765
- 18.
Li X and Guo Y H. 2013. Adaptive active learning for image classification//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Portland: IEEE: 859-866
- 19.
Li X and Ye J A. 1997. Accuracy improvement of land use change detection using principal components analysis: a case study in the Pearl River Delta. Journal of Remote Sensing (in Chinese), 1(4): 282-289
- 20.
Liang J T, Chen C, Sun W W, Yang G, Liu Z S and Zhang Z L. 2023. Spatio-temporal land use/cover change dynamics in Hangzhou Bay, China, using long-term Landsat time series and GEE platform. National Remote Sensing Bulletin, 27(6): 1480-1495
- 21.
Liang W, Liang Y Z and Jia J G. 2023. MiAMix: enhancing image classification through a multi-stage augmented mixed sample data augmentation method. Processes, 11(12): 3284
- 22.
Lin C J, Jeng S Y and Chen M K. 2020. Using 2D CNN with Taguchi parametric optimization for lung cancer recognition from CT images. Applied Sciences, 10(7): 2591
- 23.
Liu H, Gong P, Wang J, Wang X, Ning G and Xu B. 2021. Production of global daily seamless data cubes and quantification of global land cover change from 1985 to 2020-iMap World 1.0. Remote Sensing of Environment, 258: 112364
- 24.
Liu S J, Shi Q and Zhang L P. 2020. Few-shot hyperspectral image classification with unknown classes using multitask deep learning. IEEE Transactions on Geoscience and Remote Sensing, 59(6): 5085-5102
- 25.
Liu Z G, Shi W Z, Li D R and Qin Q Q. 2005. Partially supervised classification of remotely sensed imagery using support vector machines. Journal of Remote Sensing (in Chinese), 9(4): 363-373
- 26.
Loh W Y. 2011. Classification and regression trees. WIREs Data Mining and Knowledge Discovery, 1(1): 14-23
- 27.
Luo Z, Li M and Zhang D C. 2022. Building detection based on a boundary-regulated network and watershed segmentation. National Remote Sensing Bulletin, 26(7): 1459-1468
- 28.
Lv Z Y, Zhang P F, Sun W W, Benediktsson J A and Lei T. 2023a. Novel land-cover classification approach with nonparametric sample augmentation for hyperspectral remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 61: 4407613
- 29.
Lv Z Y, Zhang P F, Sun W W, Benediktsson J A, Li J H and Wang W. 2023b. Novel adaptive region spectral-spatial features for land cover classification with high spatial resolution remotely sensed imagery. IEEE Transactions on Geoscience and Remote Sensing, 61: 5609412
- 30.
Lv Z Y, Zhang P F, Sun W W, Lei T, Benediktsson J A and Li P. 2024. Sample iterative enhancement approach for improving classification performance of hyperspectral imagery. IEEE Geoscience and Remote Sensing Letters, 21: 2500605
- 31.
Mateo-García G, Gómez-Chova L, Amorós-López J, Muñoz-Marí J and Camps-Valls G. 2018. Multitemporal cloud masking in the Google Earth Engine. Remote Sensing, 10(7): 1079
- 32.
Naboureh A, Li A N, Ebrahimy H, Bian J H, Azadbakht M, Amani M, Lei G B and Nan X. 2021. Assessing the effects of irrigated agricultural expansions on Lake Urmia using multi-decadal Landsat imagery and a sample migration technique within Google Earth Engine. International Journal of Applied Earth Observation and Geoinformation, 105: 102607
- 33.
Pal M. 2005. Random forest classifier for remote sensing classification. International Journal of Remote Sensing, 26(1): 217-222
- 34.
Qin D D, Wan L, He P E, Zhang Y, Guo Y and Chen J. 2022. Multiscale object detection in remote sensing image by combining data fusion and feature selection. National Remote Sensing Bulletin, 26(8): 1662-1673
- 35.
Richards J A and Jia X P. 2008. Using suitable neighbors to augment the training set in hyperspectral maximum likelihood classification. IEEE Geoscience and Remote Sensing Letters, 5(4): 774-777
- 36.
Samat A, Gamba P, Liu S C, Du P J and Abuduwaili J. 2016. Jointly informative and manifold structure representative sampling based active learning for remote sensing image classification. IEEE Transactions on Geoscience and Remote Sensing, 54(11): 6803-6817
- 37.
Su Y C, Xu R Q, Gao L R, Han Z and Sun X. 2024. Development of deep learning-based hyperspectral remote sensing image unmixing. National Remote Sensing Bulletin, 28(1): 1-19
- 38.
Sudmanns M, Tiede D, Wendt L and Baraldi A. 2017. Automatic ex-post flood assessment using long time series of optical earth observation images. Journal for Geographic Information Science, 5(1): 217-227
- 39.
Tang W S, Long G D, Liu L, Zhou T Y, Jiang J and Blumenstein M. 2020. Rethinking 1d-cnn for time series classification: a stronger baseline. arXiv preprint arXiv: 2002.10061.
- 40.
Wambugu N, Chen Y P, Xiao Z L, Tan K, Wei M Q, Liu X X and Li J. 2021. Hyperspectral image classification on insufficient-sample and feature learning using deep neural networks: a review. International Journal of Applied Earth Observation and Geoinformation, 105: 102603
- 41.
Wang C, Zhang L, Wei W and Zhang Y N. 2020. Hyperspectral image classification with data augmentation and classifier fusion. IEEE Geoscience and Remote Sensing Letters, 17(8): 1420-1424
- 42.
Wang G A, Hwang J N, Rose C and Wallace F. 2019. Uncertainty-based active learning via sparse modeling for image classification. IEEE Transactions on Image Processing, 28(1): 316-329
- 43.
Wang L Q, Zhou J L, Li Z W, Zhao X, Wu C L and Xu M M. 2023. Adversarial MixUp with implicit semantic preservation for semi-supervised hyperspectral image classification. Signal Processing, 211: 109116
- 44.
Wang W Q, Chen Y S, He X and Li Z K. 2022. Soft augmentation-based Siamese CNN for hyperspectral image classification with limited training samples. IEEE Geoscience and Remote Sensing Letters, 19: 5508505
- 45.
Wu H and Prasad S. 2018. Semi-supervised deep learning using pseudo labels for hyperspectral image classification. IEEE Transactions on Image Processing, 27(3): 1259-1270
- 46.
Xia P P, Zhang L and Li F Z. 2015. Learning similarity with cosine similarity ensemble. Information Sciences, 307: 39-52
- 47.
Xue Z H, Zhou Y Y and Du P J. 2022. S3Net: spectral–spatial Siamese network for few-shot hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 60: 5531219
- 48.
Yang B, Mao Y, Chen J, Liu J Q, Chen J and Yan K. 2023. Review of remote sensing change detection in deep learning: bibliometric and analysis. National Remote Sensing Bulletin, 27(9): 1988-2005
- 49.
Yang X, Fang L Y and Yue J. 2024. Advances in semi-supervised classification of hyperspectral remote sensing images. National Remote Sensing Bulletin, 28(1): 20-41
- 50.
Yao Y Q, Cheng G, Xie X X and Han J W. 2021. Optical remote sensing image object detection based on multi-resolution feature fusion. National Remote Sensing Bulletin, 25(5): 1124-1137
- 51.
Yu Y, Ai H, He X J, Yu S H, Zhong X and Zhu R F. 2021. Attention-based feature pyramid networks for ship detection of optical remote sensing image. National Remote Sensing Bulletin, 24(2): 107-115
- 52.
Zhang L, Liao J J, Yuan X, Mu X D, Song X X and Bi J P. 2020. Remote sensing analysis of coastline changes in Hainan Island during 1987-2017. Tropical Geography, 40(4): 659-674
- 53.
Zhang L, Luo W T, Zhang H H, Yin X W and Li B. 2023. Classification scheme for mapping wetland herbaceous plant communities using time series Sentinel-1 and Sentinel-2 data. National Remote Sensing Bulletin, 27(6): 1362-1375
- 54.
Zhang S, Li S S, Wei G F, Zhang X N and Gao J W. 2022. Refined multi-scale feature-oriented object detection of remote sensing images. National Remote Sensing Bulletin, 26(12): 2616-2628
- 55.
Zhang S C. 2012. Nearest neighbor selection for iteratively kNN imputation. Journal of Systems and Software, 85(11): 2541-2552
- 56.
Zhang Y J, Gao Y X, Huang H and Ren L L. 2006. Research on remote sensing classification of urban vegetation species based on SVM decision-making tree. Journal of Remote Sensing (in Chinese), (2): 191-196
- 57.
Zhang Y Q, Cao G, Shafique A and Fu P. 2019. Label propagation ensemble for hyperspectral image classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(9): 3623-3636
- 58.
Zhao L J and Tang P. 2016. Scalability analysis of typical remote sensing data classification methods: a case of remote sensing image scene. Journal of Remote Sensing (in Chinese), 20(2): 157-171
- 59.
Zheng J H, Sun C, Lin Y, Li L and Liu Y C. 2023. Classification of salt marsh vegetation based on pixel-level time series from Landsat images. National Remote Sensing Bulletin, 27(6): 1400-1413
- 60.
Zhong B, Yang A X, Jue K and Wu J J. 2021. Long time series high-quality and high-consistency land cover mapping based on machine learning method at heihe river basin. Remote Sensing, 13(8): 1596
- 61.
Zurqani H A, Post C J, Mikhailova E A, Schlautman M A and Sharp J L. 2018. Geospatial analysis of land use change in the Savannah River Basin using Google Earth Engine. International Journal of Applied Earth Observation and Geoinformation, 69: 175-185