[1] |
Reese D C, O'Malley R T, Brodeur R D, et al. Epipelagic fish distributions in relation to thermal fronts in a coastal upwelling system high-resolution remote-sensing techniques[J]. ICES Journal of Marine Science, 2019, 68(9): 1865-1874.
doi: 10.1093/icesjms/fsr107
|
[2] |
Lei F, Yu Y, Zhang D J, et al. Water remote sensing eutrophication inversion algorithm based on multilayer convolutional neural network[J]. Journal of Intelligent and Fuzzy System, 2020, 39(4): 5319-5227.
doi: 10.3233/JIFS-189017
|
[3] |
Dong Y N, Liang T Y, Zhang Y X, et al. Spectral-spatial weighted kernel manifold embedded distribution alignment for remote sensing image classification[J]. IEEE Transactions on Cybernetics, 2021, 51(6): 3185-3197.
doi: 10.1109/TCYB.2020.3004263
|
[4] |
Ding L, Tang H, Bruzzone L. LANet: local attention embedding to improve the semantic segmentation on remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(1): 426-435.
doi: 10.1109/TGRS.2020.2994150
|
[5] |
Di L P, Zhong Y F, Yao Y Y, et al. Spark-based adaptive mapreduce data processing method for remote sensing imagery[J]. International Journal of Remote Sensing, 2021, 42(1): 171-187.
|
[6] |
Xiao Q T, Zhong X, Zhong C H. Application research of KNN algorithm based on clustering in big data talent demand information classification[J]. International Journal of Pattern Recognition and Artificial Intelligence, 2020, 34(6): 2050015.
doi: 10.1142/S0218001420500159
|
[7] |
Altunkaynak A, Jalilzadnezamabad A. Extended lead time accurate forecasting of plamer drought severity index using hybid wavelet-fuzzy and machine learning techniques[J]. Journal of Hydrology, 2021, 601(3): 126619.
doi: 10.1016/j.jhydrol.2021.126619
|
[8] |
仉文岗, 李红蕊, 巫崇智, 等. 基于 RF 和 KNN 的地下采场开挖稳定性评估[J]. 湖南大学学报(自然科学版), 2021, 48(3): 164-172.
|
[9] |
叶利华, 王磊, 张文文, 等. 高分辨率光学遥感场景分类的深度度量学习方法[J]. 测绘学报, 2019, 48(6): 698-707.
|
[10] |
于挺, 杨军. 基于 $K$ 近邻卷积神经网络的点云模型识别与分类[J]. 激光与光电子学进展, 2020, 57(10): 101510.
|
[11] |
Borgi A, Akdag H. Knowledge based supervised fuzzy-classification: an application to image processing[J]. Annals of Mathematics and Artificial Intelligence, 2018, (32): 67-86.
|
[12] |
Maulik U, Chakraborty D. A self-trained ensemble with semisupervised SVM: an application to pixel classification of remote sensing imagery[J]. Pattern Recognition, 2019, 44(3): 615-623.
doi: 10.1016/j.patcog.2010.09.021
|
[13] |
Mountrakis G, Irn J, Ogole C. Support vector machines in remote sensing: a review[J]. ISPRS Journal of Photogremmetry and Remote Sensing, 2018, 66(3): 247-259.
|
[14] |
LI F F. A Bayesian approach to unsupervised one-shot learning of object categories[C]// 2003 IEEE International Conference on Computer Vision. 2003: 1134-1141.
|
[15] |
Li F F, Fergus R, Perona P. One-shot learning of object categories[J]. IEEE Transaction on Pattern Analysis & Machine Intelligence, 2006, 28(4): 594-611.
|
[16] |
Ake B M, Salakhutdinov R, Tenenbaum J B. Human-level concept learning through probabilistic program induction[J]. Science, 2015, 350(6266): 1332-1338.
doi: 10.1126/science.aab3050
|
[17] |
Koch G, Zemel R, Salakhutdinov R. Siamese neural networks for one-shot image recog- nition[C]// 32nd International Conference on Machine Learning. 2015: 2-15.
|
[18] |
Vinyale O, Blundell C, Lillicrap T, et al. Matching networks for one shot learning[C]// 30nd International Conference on Neural Information Processing System. 2016: 3630-3638.
|
[19] |
Snell J, Swersky K, Zemel R. Prototypical networks for few-shot learning[C]// 31nd International Conference on Neural Information Processing System. 2017: 4077-4087.
|
[20] |
Li W, Xu J, Huo J, et al. Distribution consistency based covariance metric networks for few-shot learning[C]// 33th AAAI Conference on Artificial Intelligence. 2019: 458-472.
|
[21] |
Finn C, Abbeel P, Levine S. Model-agnostic meta-learning for fast adaptation of deep networks[C]// 34th International Conference on Machine Learning. 2017: 1126-1135.
|
[22] |
Ravichandran A, Bhotika R, Soatto S. Few-shot learning with embedded class models and shot-free meta training[C]// 2019 International Conference on Computer Vision. 2019: 331-339.
|
[23] |
Sun Q, Liu Y, Chua T, et al. Meta-transfer learning for few-shot learning[C]// 32th IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019: 403-412.
|
[24] |
Chen H, Wang Y, Wang G, et al. LSTD: a low-shot transfer detector for object detection[C]// 32th AAAI Conference on Artificial Intelligence. 2018: 258-274.
|
[25] |
Wang T, Zhang X, Yuan L, et al. Few-shot adaptive faster R-CNN[C]// 32th IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019: 7173-7182.
|
[26] |
Chen W, Liu Y, Kira Z, et al. A Closer look at few-shot classification[C]// International Conference on Learning Representation. 2019: 542-551.
|