地球信息科学学报 ›› 2023, Vol. 25 ›› Issue (5): 1050-1063.doi: 10.12082/dqxxkx.2023.220781
收稿日期:
2022-10-12
修回日期:
2023-01-28
出版日期:
2023-05-25
发布日期:
2023-04-27
通讯作者:
*刘 智(1965— ),男,湖南长沙人,教授,主要从事遥感图像智能解译研究。 E-mail: zhiliu001@sina.com作者简介:
刘 潇(1999— ),女,山东临沂人,硕士生,主要从事遥感图像智能解译研究。 E-mail: liuxiao99919@163.com
LIU Xiao(), LIU Zhi(
), LIN Yuzhun, WANG Shuxiang, ZUO Xibing
Received:
2022-10-12
Revised:
2023-01-28
Online:
2023-05-25
Published:
2023-04-27
Contact:
LIU Zhi
摘要:
卷积神经网络已广泛应用于遥感影像场景分类任务,然而优秀的模型体量大,无法部署到资源受限的边缘设备中,直接应用现有的知识蒸馏方法压缩模型,忽略了场景数据的类内多样性和类间相似性。为此,本文提出一种类中心知识蒸馏方法,旨在获得一个紧凑高效且精度高的遥感影像场景分类网络。首先对预训练的教师网络进行微调,然后基于设计的类中心蒸馏损失将教师网络强大的特征提取能力迁移到学生网络,通过约束师生网络提取的同类特征分布中心的距离完成知识的转移,同时在蒸馏过程中结合真值标签训练,最后学生网络单独用于预测。实验在4个数据集上与8种先进的蒸馏方法在不同训练比率、不同师生架构下进行了比较,本文方法均达到最高分类精度。其中,在训练比率为60%的RSC11、UCM、RSSCN7及AID数据集中,相比于性能最好的其他蒸馏方法,师生网络属同系列时分类总体精度分别提升了2.42%、2.74%、2.95%和1.07%。相似技术对比实验及可视化分析进一步证明了本文方法优异的性能。本文所提出的类中心知识蒸馏方法更好地传递了复杂网络所提取的类内紧凑、类间离散的特征知识,提高了轻量网络分类的性能。
刘潇, 刘智, 林雨准, 王淑香, 左溪冰. 面向遥感影像场景分类的类中心知识蒸馏方法[J]. 地球信息科学学报, 2023, 25(5): 1050-1063.DOI:10.12082/dqxxkx.2023.220781
LIU Xiao, LIU Zhi, LIN Yuzhun, WANG Shuxiang, ZUO Xibing. Class-centric Knowledge Distillation for RSI Scene Classification[J]. Journal of Geo-information Science, 2023, 25(5): 1050-1063.DOI:10.12082/dqxxkx.2023.220781
表3
UCM数据集上各种知识蒸馏方法的总体精度
蒸馏方法 | 师生架构 (Model T/S) | 同系列 (ResNet-50/ ResNet-18) | 不同系列 (ResNet-50/MobileNet-V2) | ||
---|---|---|---|---|---|
训练比率 | 80% | 60% | 80% | 60% | |
Baseline | 92.14 | 90.00 | 91.43 | 90.48 | |
响应 | KD | 95.48 | 92.38 | 93.33 | 90.24 |
DKD | 95.71 | 91.91 | 94.52 | 92.14 | |
特征 | NST | 94.05 | 91.67 | 92.62 | 90.48 |
VID | 93.57 | 89.41 | 92.38 | 89.05 | |
网络层间关系 | KDSVD | 92.62 | 90.36 | 92.62 | 89.88 |
ReviewKD | 94.29 | 92.62 | 94.29 | 91.07 | |
实例关系 | RKD | 94.52 | 92.02 | 92.38 | 87.38 |
SP | 93.33 | 91.43 | 80.48 | 59.05 | |
类中心 | 本文方法 | 97.14 | 95.36 | 94.76 | 92.62 |
表5
多个数据集在60%训练比率及同构师生网络条件下的总体精度
数据集 | 教师网络 | 学生网络 | 响应 | 特征 | 网络层间关系 | 实例关系 | 类中心 | ||||
---|---|---|---|---|---|---|---|---|---|---|---|
KD | DKD | NST | VID | KDSVD | ReviewKD | RKD | SP | 本文方法 | |||
RS_C11 | 92.35 | 90.14 | 91.95 | 90.95 | 89.74 | 88.13 | 87.73 | 90.34 | 89.74 | 87.73 | 94.37 |
RSSCN7 | 91.07 | 88.75 | 88.30 | 87.32 | 87.59 | 87.41 | 87.05 | 88.21 | 88.75 | 86.52 | 91.70 |
AID | 95.68 | 88.98 | 92.73 | 93.03 | 91.43 | 88.96 | 89.20 | 92.58 | 91.45 | 91.65 | 94.10 |
表7
对比实验在RSC11数据集上的精度混淆矩阵
单独训练的学生网络 | 微调的教师网络 | ||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
密林 | 草地 | 港口 | 高建筑 | 低建筑 | 立交 | 铁路 | 居民区 | 公路 | 疏林 | 储存罐 | 密林 | 草地 | 港口 | 高建筑 | 低建筑 | 立交 | 铁路 | 居民区 | 公路 | 疏林 | 储存罐 | ||
密林 | 98.21 | 1.79 | 密林 | 100 | |||||||||||||||||||
草地 | 100 | 草地 | 100 | ||||||||||||||||||||
港口 | 97.22 | 2.78 | 港口 | 100 | |||||||||||||||||||
高建筑 | 91.11 | 2.22 | 2.22 | 4.44 | 高建筑 | 97.62 | 2.38 | ||||||||||||||||
低建筑 | 85.42 | 6.25 | 8.33 | 地建筑 | 91.67 | 4.17 | 2.08 | 2.08 | |||||||||||||||
立交 | 2.33 | 74.42 | 2.33 | 18.6 | 2.33 | 立交 | 2.27 | 68.18 | 9.09 | 20.45 | |||||||||||||
铁路 | 3.12 | 3.12 | 87.5 | 6.25 | 铁路 | 8.00 | 88.00 | 4.00 | |||||||||||||||
居民区 | 1.61 | 1.61 | 87.1 | 4.84 | 4.84 | 居民区 | 1.79 | 98.21 | |||||||||||||||
公路 | 1.89 | 1.89 | 9.43 | 3.77 | 83.02 | 公路 | 3.28 | 13.11 | 8.20 | 75.41 | |||||||||||||
疏林 | 2.17 | 97.83 | 疏林 | 100 | |||||||||||||||||||
储存罐 | 5.71 | 2.86 | 91.43 | 储存罐 | 100 | ||||||||||||||||||
基于改进NST方法训练的学生网络 | 基于本文方法训练的学生网络 | ||||||||||||||||||||||
密林 | 草地 | 港口 | 高建筑 | 低建筑 | 立交 | 铁路 | 居民区 | 公路 | 疏林 | 储存罐 | 密林 | 草地 | 港口 | 高建筑 | 低建筑 | 立交 | 铁路 | 居民区 | 公路 | 疏林 | 储存罐 | ||
密林 | 100 | 密林 | 100 | ||||||||||||||||||||
草地 | 100 | 草地 | 100 | ||||||||||||||||||||
港口 | 92.31 | 2.56 | 5.13 | 港口 | 100 | ||||||||||||||||||
高建筑 | 90.91 | 4.55 | 2.27 | 2.27 | 高建筑 | 97.73 | 2.27 | ||||||||||||||||
地建筑 | 2.50 | 95.00 | 2.5 | 低建筑 | 95.24 | 2.38 | 2.38 | ||||||||||||||||
立交 | 89.19 | 5.41 | 5.41 | 立交 | 86.11 | 5.56 | 2.78 | 5.56 | |||||||||||||||
铁路 | 3.12 | 3.12 | 87.5 | 3.12 | 3.12 | 铁路 | 3.33 | 6.67 | 90.00 | ||||||||||||||
居民区 | 1.67 | 91.67 | 3.33 | 3.33 | 居民区 | 1.69 | 1.69 | 1.69 | 93.22 | 1.69 | |||||||||||||
公路 | 14.75 | 3.28 | 81.97 | 公路 | 15.15 | 3.03 | 81.82 | ||||||||||||||||
疏林 | 2.17 | 97.83 | 疏林 | 100 | |||||||||||||||||||
储存罐 | 7.14 | 92.86 | 储存罐 | 2.33 | 97.67 |
[1] |
Ghazouani F, Farah I R, Solaiman B. A multi-level semantic scene interpretation strategy for change interpretation in remote sensing imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(11):8775-8795. DOI:10.1109/TGRS.2019.2922908
doi: 10.1109/TGRS.2019.2922908 |
[2] |
Hu F, Xia G S, Hu J W, et al. Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery[J]. Remote Sensing, 2015, 7(11):14680-14707. DOI:10.3390/rs71114680
doi: 10.3390/rs71114680 |
[3] |
Gu Y T, Wang Y T, Li Y S. A survey on deep learning-driven remote sensing image scene understanding: Scene classification, scene retrieval and scene-guided object detection[J]. Applied Sciences, 2019, 9(10):2110. DOI:10.3390/app9102110
doi: 10.3390/app9102110 |
[4] |
Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7):971-987. DOI: 10.1109/TPAMI.2002.1017623
doi: 10.1109/TPAMI.2002.1017623 |
[5] |
Zhu Q Q, Zhong Y F, Zhao B, et al. Bag-of-visual-words scene classifier with local and global features for high spatial resolution remote sensing imagery[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(6):747-751. DOI:10.1109/LGRS.2015.2513443
doi: 10.1109/LGRS.2015.2513443 |
[6] |
Romero A, Gatta C, Camps-Valls G. Unsupervised deep feature extraction for remote sensing image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(3):1349-1362. DOI:10.1109/TGRS.2015.2478379
doi: 10.1109/TGRS.2015.2478379 |
[7] |
Cheng G, Xie X X, Han J W, et al. Remote sensing image scene classification meets deep learning: Challenges, methods, benchmarks, and opportunities[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13:3735-3756. DOI:10.1109/JSTARS.2020.3005403
doi: 10.1109/JSTARS.2020.3005403 |
[8] |
郭子慧, 刘伟. 深度学习和遥感影像支持的矢量图斑地类解译真实性检查方法[J]. 地球信息科学学报, 2020, 22(10):2051-2061.
doi: 10.12082/dqxxkx.2020.200001 |
[ Guo Z H, Liu W. Land type interpretation authenticity check of vector patch supported by deep learning and remote sensing image[J]. Journal of Geo-Information Science, 2020, 22(10):2051-2061. ] DOI:10.12082/dqxxkx.2020.200001
doi: 10.12082/dqxxkx.2020.200001 |
|
[9] | 余东行, 张保明, 赵传, 等. 联合卷积神经网络与集成学习的遥感影像场景分类[J]. 遥感学报, 2020, 24(6):717-727. |
[ Yu D H, Zhang B M, Zhao C, et al. Scene classification of remote sensing image using ensemble convolutional neural network[J]. Journal of Remote Sensing, 2020, 24(6):717-727. ] DOI:10.11834/jrs.20208273
doi: 10.11834/jrs.20208273 |
|
[10] |
Sun H M, Lin Y W, Zou Q, et al. Convolutional neural networks based remote sensing scene classification under clear and cloudy environments[C]// 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW). IEEE, 2021:713-720. DOI:10.1109/ICCVW54120.2021.00085
doi: 10.1109/ICCVW54120.2021.00085 |
[11] |
Chen W T, Ouyang S B, Tong W, et al. GCSANet: A global context spatial attention deep learning network for remote sensing scene classification[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15:1150-1162. DOI:10.1109/JSTARS.2022.3141826
doi: 10.1109/JSTARS.2022.3141826 |
[12] |
Wang Q, Huang W, Xiong Z T, et al. Looking closer at the scene: Multiscale representation learning for remote sensing image scene classification[J]. IEEE Transactions on Neural Networks and Learning Systems, 2022, 33(4):1414-1428. DOI:10.1109/TNNLS.2020.3042276
doi: 10.1109/TNNLS.2020.3042276 |
[13] |
彭瑞, 赵文智, 张立强, 等. 基于多尺度对比学习的弱监督遥感场景分类[J]. 地球信息科学学报, 2022, 24(7):1375-1390.
doi: 10.12082/dqxxkx.2022.210809 |
[ Peng R, Zhao W Z, Zhang L Q, et al. Multi-scale contrastive learning based weakly supervised learning for remote sensing scene classification[J]. Journal of Geo-Information Science, 2022, 24(7):1375-1390. ] DOI:10.12082/dqxxkx.2022.210809
doi: 10.12082/dqxxkx.2022.210809 |
|
[14] |
Zhang R, Chen Z H, Zhang S X, et al. Remote sensing image scene classification with noisy label distillation[J]. Remote Sensing, 2020, 12(15): 2376. DOI:10.3390/rs12 152376
doi: 10.3390/rs12010012 |
[15] |
Zhang B, Zhang Y J, Wang S G. A lightweight and discriminative model for remote sensing scene classification with multidilation pooling module[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(8):2636-2653. DOI:10.1109/JSTARS.2019.2919317
doi: 10.1109/JSTARS.2019.2919317 |
[16] |
Chen G Z, Zhang X D, Tan X L, et al. Training small networks for scene classification of remote sensing images via knowledge distillation[J]. Remote Sensing, 2018, 10(5):719. DOI:10.3390/rs10050719
doi: 10.3390/rs10050719 |
[17] |
Zhao H R, Sun X, Gao F, et al. Pair-wise similarity knowledge distillation for RSI scene classification[J]. Remote Sensing, 2022, 14(10):2483. DOI: 10.3390/rs14102483
doi: 10.3390/rs14102483 |
[18] | Li H, Kadav A, Durdanovic I, et al. Pruning filters for efficient ConvNets[EB/OL]. 2016: arXiv: 1608.08710. https://arxiv.org/abs/1608.08710 |
[19] |
Yang T J, Chen Y H, Sze V. Designing energy-efficient convolutional neural networks using energy-aware pruning[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2017:6071-6079. DOI:10.1109/CVPR.2017.643
doi: 10.1109/CVPR.2017.643 |
[20] |
Gou J P, Yu B S, Maybank S J, et al. Knowledge distillation: A survey[J]. International Journal of Computer Vision, 2021, 129(6):1789-1819. DOI:10.1007/s11263-021-01453-z
doi: 10.1007/s11263-021-01453-z |
[21] |
Buciluǎ C, Caruana R, Niculescu-Mizil A. Model compression[C]// Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. New York: ACM, 2006:535-541. DOI:10.1145/1150402.1150464
doi: 10.1145/1150402.1150464 |
[22] |
Ba J and Caruana R. Do deep nets really need to be deep?[C]. Advances in Neural Information Processing Systems, 2014, 27. DOI:10.48550/arXiv.1312.6184
doi: 10.48550/arXiv.1312.6184 |
[23] | Hinton G, Vinyals O, Dean J. Distilling the knowledge in a neural network[EB/OL]. 2015: arXiv:1503.02531. https://arxiv.org/abs/1503.02531 |
[24] | Romero A, Ballas N, Kahou S E, et al. FitNets: Hints for thin deep nets[EB/OL]. 2014: arXiv:1412.6550. https://arxiv.org/abs/1412.6550 |
[25] | Zagoruyko S, Komodakis N. Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer[EB/OL]. 2016: arXiv: 1612.03928. https://arxiv.org/abs/1612.03928 |
[26] | Huang Z H, Wang N Y. Like what You like: Knowledge distill via neuron selectivity transfer[EB/OL]. 2017:arXiv: 1707.01219. https://arxiv.org/abs/1707.01219 |
[27] | Kim J, Park S, Kwak N. Paraphrasing complex network: Network compression via factor transfer[EB/OL]. 2018: arXiv: 1802.04977. https://arxiv.org/abs/1802.04977 |
[28] |
Heo B, Kim J, Yun S, et al. A comprehensive overhaul of feature distillation[C]// 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2020:1921- 1930. DOI:10.1109/ICCV.2019.00201
doi: 10.1109/ICCV.2019.00201 |
[29] |
Ahn S, Hu S X, Damianou A, et al. Variational information distillation for knowledge transfer[C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020:9155-9163. DOI:10.1109/CVPR.2019.00938
doi: 10.1109/CVPR.2019.00938 |
[30] |
Yim J, Joo D, Bae J, et al. A gift from knowledge distillation: Fast optimization, network minimization and transfer learning[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2017:7130-7138. DOI:10.1109/CVPR.2017.754
doi: 10.1109/CVPR.2017.754 |
[31] |
Lee S H, Kim D H, Song B C. Self-supervised knowledge distillation using singular value decomposition[M]//Computer Vision - ECCV 2018. Cham: Springer International Publishing, 2018:339-354. DOI:10.1007/978-3-030-01231-1_21
doi: 10.1007/978-3-030-01231-1_21 |
[32] |
Chen P G, Liu S, Zhao H S, et al. Distilling knowledge via knowledge review[C]// 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2021:5006-5015. DOI: 10.1109/CVPR46437.2021.00497
doi: 10.1109/CVPR46437.2021.00497 |
[33] |
Passalis N, Tefas A. Learning deep representations with probabilistic knowledge transfer[M]//Computer Vision - ECCV 2018. Cham: Springer International Publishing, 2018:283-299. DOI:10.1007/978-3-030-01252-6_17
doi: 10.1007/978-3-030-01252-6_17 |
[34] |
Tung F, Mori G. Similarity-preserving knowledge distillation[C]// 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2020:1365-1374. DOI: 10.1109/ICCV.2019.00145
doi: 10.1109/ICCV.2019.00145 |
[35] |
Park W, Kim D, Lu Y, et al. Relational knowledge distillation[C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020:3962-3971. DOI: 10.1109/CVPR.2019.00409
doi: 10.1109/CVPR.2019.00409 |
[36] |
Zhao B R, Cui Q, Song R J, et al. Decoupled knowledge distillation[C]// 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2022:11943-11952. DOI: 10.1109/CVPR52688.2022.01165
doi: 10.1109/CVPR52688.2022.01165 |
[37] |
Wang L, Yoon K J. Knowledge distillation and student-teacher learning for visual intelligence: A review and new outlooks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(6):3048-3068. DOI:10.1109/TPAMI.2021.3055564
doi: 10.1109/TPAMI.2021.3055564 |
[38] | 杨宏炳, 迟勇欣, 王金光. 基于剪枝网络的知识蒸馏对遥感卫星图像分类方法[J]. 计算机应用研究, 2021, 38(8):2469-2473. |
[ Yang H B, Chi Y X, Wang J G. Knowledge distillation method for remote sensing satellite image classification based on pruning network[J]. Application Research of Computers, 2021, 38(8):2469-2473. ] DOI: 10.19734/j.issn.1001-3695.2020.07.0387
doi: 10.19734/j.issn.1001-3695.2020.07.0387 |
|
[39] | Gretton A, Borgwardt K M, Rasch M J, et al. A kernel two-sample test[J]. Journal of Machine Learning Research, 2012, 13(25):723-773. |
[40] | Sejdinovic D, Gretton A. What is an RKHS? Lecture Notes, 2012 |
[41] |
Zhao L J, Tang P, Huo L Z. Feature significance-based multibag-of-visual-words model for remote sensing image scene classification[J]. Journal of Applied Remote Sensing, 2016, 10(3):035004. DOI:10.1117/1.JRS.10.035004
doi: 10.1117/1.JRS.10.035004 |
[42] |
Zou Q, Ni L H, Zhang T, et al. Deep learning based feature selection for remote sensing scene classification[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(11):2321-2325. DOI:10.1109/LGRS.2015.2475299
doi: 10.1109/LGRS.2015.2475299 |
[43] |
Liu B D, Xie W Y, Meng J, et al. Hybrid collaborative representation for remote-sensing image scene classification[J]. Remote Sensing, 2018, 10(12): 1934. DOI:10.3390/rs10121934
doi: 10.3390/rs10121934 |
[44] |
Xia G S, Hu J W, Hu F, et al. AID: A benchmark data set for performance evaluation of aerial scene classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(7):3965-3981. DOI:10.1109/TGRS.2017.2685945
doi: 10.1109/TGRS.2017.2685945 |
[45] |
Xie S N, Girshick R, Dollár P, et al. Aggregated residual transformations for deep neural networks[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2017:5987-5995. DOI:10.1109/CVPR.2017.634
doi: 10.1109/CVPR.2017.634 |
[46] |
Sandler M, Howard A, Zhu M L, et al. MobileNetV2: inverted residuals and linear bottlenecks[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 2018:4510-4520. DOI:10.1109/CVPR.2018.00474
doi: 10.1109/CVPR.2018.00474 |
[47] | Maaten L van der and Hinton G. Visualizing data using t-sne[J]. Journal of Machine Learning Research, 2008, 9(86):2579-2605. |
[48] |
Selvaraju R R, Cogswell M, Das A, et al. Grad-CAM: Visual explanations from deep networks via gradient-based localization[C]// 2017 IEEE International Conference on Computer Vision (ICCV). IEEE, 2017:618-626. DOI: 10.1109/ICCV.2017.74
doi: 10.1109/ICCV.2017.74 |
[1] | 范兰馨, 吴艳红, 迟皓婧, 郑思齐, 闫家恒, 任永康, 孙忠华. 暖湿化下西北地区水体变化趋势遥感监测[J]. 地球信息科学学报, 2023, 25(9): 1842-1854. |
[2] | 邢子瑶, 董芯蕊, 昝糈莉, 杨帅, 黄梓焓, 刘哲, 张晓东. 融合VGI和遥感等多源数据的洪涝范围提取与模拟方法[J]. 地球信息科学学报, 2023, 25(9): 1869-1881. |
[3] | 张俊瑶, 杨晓梅, 王志华, 杨海坤, 张博淳, 万庆, 雷梅. 绿色发展理念视角下内蒙古煤矿区格局演变分析[J]. 地球信息科学学报, 2023, 25(8): 1655-1668. |
[4] | 曹煜, 方秀琴, 杨露露, 蒋心远, 廖美玉, 任立良. 基于随机森林的西辽河流域CCI土壤湿度降尺度研究[J]. 地球信息科学学报, 2023, 25(8): 1669-1681. |
[5] | 张寒博, 李彤, 李晓芳, 邓滢, 邓应彬, 荆文龙, 胡义强, 李勇, 杨骥. 无人机遥感和GWR结合的水华短时预测方法[J]. 地球信息科学学报, 2023, 25(8): 1682-1698. |
[6] | 左溪冰, 刘智, 金飞, 林雨准, 王淑香, 刘潇, 李美霖. 面向高光谱影像小样本分类的全局-局部特征自适应融合方法[J]. 地球信息科学学报, 2023, 25(8): 1699-1716. |
[7] | 林娜, 何静, 王斌, 唐菲菲, 周俊宇, 郭江. 结合植被光谱特征与Sep-UNet的城市植被信息智能提取方法[J]. 地球信息科学学报, 2023, 25(8): 1717-1729. |
[8] | 张春菊, 刘文聪, 张雪英, 叶鹏, 汪陈, 朱少楠, 张达玉. 基于本体的金矿知识图谱构建方法[J]. 地球信息科学学报, 2023, 25(7): 1269-1281. |
[9] | 张彤, 刘仁宇, 王培晓, 高楚林, 刘杰, 王望舒. 感知物理先验的机器学习及其在地理空间智能中的研究前景[J]. 地球信息科学学报, 2023, 25(7): 1297-1311. |
[10] | 吴敏, 张明达, 李盼盼, 张勇健. 面向多源遥感影像数据的溯源模型研究[J]. 地球信息科学学报, 2023, 25(7): 1325-1335. |
[11] | 令振飞, 刘涛, 杜萍, 赵丹, 陈朴一, 马天恩. 一种支持建筑群组相似模式检索的变分图卷积自编码模型[J]. 地球信息科学学报, 2023, 25(7): 1405-1417. |
[12] | 曹兴文, 吴孟泉, 郑雪婷, 郑宏伟, 李映祥, 张安安. 室内空间布局约束下的在线跟踪注册学习方法[J]. 地球信息科学学报, 2023, 25(7): 1418-1431. |
[13] | 赵丹, 杜萍, 刘涛, 令振飞. 融合图自编码器与GRU的城市盗窃犯罪时空分布预测模型[J]. 地球信息科学学报, 2023, 25(7): 1448-1463. |
[14] | 金广垠, 沙恒宇, 张金雷, 黄金才. 基于路段-路口联合建模的对偶图卷积网络的行程时间估计方法[J]. 地球信息科学学报, 2023, 25(7): 1500-1513. |
[15] | 杨颖频, 吴志峰, 黄启厅, 骆剑承, 吴田军, 董文, 胡晓东, 肖文菊. 协同遥感与统计数据的粤西甘蔗种植分布提取及时空分析[J]. 地球信息科学学报, 2023, 25(5): 1012-1026. |
|