Optics and Precision Engineering, Volume. 32, Issue 9, 1395(2024)
Active learning-clustering-group convolutions network for hyperspectral images classification
[1] HONG D F, GAO L R, YAO J et al. Graph convolutional networks for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 59, 5966-5978(2021).
[2] XIE W Y, ZHANG X, LI Y S et al. Weakly supervised low-rank representation for hyperspectral anomaly detection[J]. IEEE Transactions on Cybernetics, 51, 3889-3900(2021).
[3] TAN K, WANG X, NIU C et al. Vicarious calibration for the AHSI instrument of Gaofen-5 with reference to the CRCS Dunhuang test site[J]. IEEE Transactions on Geoscience and Remote Sensing, 59, 3409-3419(2021).
[4] [4] 刘英旭, 蒲春宇, 许典坤, 等. 面向高光谱影像场景分类的轻量化深度全局-局部知识蒸馏网络[J]. 光学 精密工程, 2023, 31(17): 2598-2610. doi: 10.37188/OPE.20233117.2598LIUY X, PUCH Y, XUD K, et al. Lightweight deep global-local knowledge distillation network for hyperspectral image scene classification[J]. Opt. Precision Eng., 2023, 31(17): 2598-2610.(in Chinese). doi: 10.37188/OPE.20233117.2598
[5] [5] 王碧琳, 王生生, 张哲. 面向领域自适应的部分最优传输高光谱图像分类[J]. 光学 精密工程, 2023, 31(17): 2555-2563. doi: 10.37188/OPE.20233117.2555WANGB L, WANGS S, ZHANGZH. Partial optimal transport-based domain adaptation for hyperspectral image classification[J]. Opt. Precision Eng., 2023, 31(17): 2555-2563.(in Chinese). doi: 10.37188/OPE.20233117.2555
[6] [6] 王爱丽, 丁姗姗, 刘和, 等. 空谱域适应与XGBoost结合的跨场景高光谱图像分类[J]. 光学 精密工程, 2023, 31(13): 1950-1961. doi: 10.37188/OPE.20233113.1950WANGA L, DINGSH SH, LIUH, et al. Cross-scene hyperspectral image classification combined spatial-spectral domain adaptation with XGBoost[J]. Opt. Precision Eng., 2023, 31(13): 1950-1961.(in Chinese). doi: 10.37188/OPE.20233113.1950
[7] LICCIARDI G, MARPU P R, CHANUSSOT J et al. Linear versus nonlinear PCA for the classification of hyperspectral data based on the extended morphological profiles[J]. IEEE Geoscience and Remote Sensing Letters, 9, 447-451(2012).
[8] BANDOS T V, BRUZZONE L, CAMPS-VALLS G. Classification of hyperspectral images with regularized linear discriminant analysis[J]. IEEE Transactions on Geoscience and Remote Sensing, 47, 862-873(2009).
[9] [9] 孙伟伟, 杨刚, 彭江涛, 等. 鲁棒多特征谱聚类的高光谱影像波段选择[J]. 遥感学报, 2022, 26(2): 397-405. doi: 10.11834/j.issn.1007-4619.2022.2.ygxb202202013SUNW W, YANGG, PENGJ T, et al. Robust multi-feature spectral clustering for hyperspectral band selection[J]. National Remote Sensing Bulletin, 2022, 26(2): 397-405.(in Chinese). doi: 10.11834/j.issn.1007-4619.2022.2.ygxb202202013
[10] AHMAD M, KHAN A, KHAN A M et al. Spatial prior fuzziness pool-based interactive classification of hyperspectral images[J]. Remote Sensing, 11, 1136(2019).
[11] WANG Y, YU W K, FANG Z C. Multiple kernel-based SVM classification of hyperspectral images by combining spectral, spatial, and semantic information[J]. Remote Sensing, 12, 120(2020).
[12] LUO H W, TANG Y Y, WANG Y L et al. Hyperspectral image classification based on spectral–spatial one-dimensional manifold embedding[J]. IEEE Transactions on Geoscience and Remote Sensing, 54, 5319-5340(2016).
[13] YU S Q, JIA S, XU C Y. Convolutional neural networks for hyperspectral image classification[J]. Neurocomputing, 219, 88-98(2017).
[14] ZHANG C J, LI G D, DU S H et al. Three-dimensional densely connected convolutional network for hyperspectral remote sensing image classification[J]. Journal of Applied Remote Sensing, 13, 1(2019).
[15] ROY S K, KRISHNA G, DUBEY S R et al. HybridSN: exploring 3-D-2-D CNN feature hierarchy for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 17, 277-281(2020).
[16] YAO W, LIAN C, BRUZZONE L. ClusterCNN: clustering-based feature learning for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 18, 1991-1995(2021).
[17] AHMAD M, KHAN A M, MAZZARA M et al. A fast and compact 3-D CNN for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 19, 1-5(2022).
[18] SONG W W, LI S T, FANG L Y et al. Hyperspectral image classification with deep feature fusion network[J]. IEEE Transactions on Geoscience and Remote Sensing, 56, 3173-3184(2018).
[19] ZHONG Z L, LI J, LUO Z M et al. Spectral–spatial residual network for hyperspectral image classification: a 3-D deep learning framework[J]. IEEE Transactions on Geoscience and Remote Sensing, 56, 847-858(2018).
[20] ROY S K, MANNA S, SONG T C et al. Attention-based adaptive spectral-spatial kernel ResNet for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 59, 7831-7843(2021).
[21] GUO W H, YE H L, CAO F L. Feature-grouped network with spectral-spatial connected attention for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-13(2022).
[22] ZHANG X R, SHANG S W, TANG X et al. Spectral partitioning residual network with spatial attention mechanism for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-14(2022).
[23] ZHAO Z G, WANG H, YU X C. Spectral–spatial graph attention network for semisupervised hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 19, 1-5(2022).
[24] [24] 翁汪磊, 孙伟伟, 任凯, 等. 高光谱影像领域自适应分类方法的对比分析[J]. 遥感技术与应用, 2023, 1–11.WENGW L, SUNW W, RENK, et al.. Comparison of domain adaptation methods for hyperspectral image classification[J]. Remote Sensing Technology and Application, 2023, 1-11. (in Chinese)
[25] CAO X Y, YAO J, XU Z B et al. Hyperspectral image classification with convolutional neural network and active learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 58, 4604-4616(2020).
[26] LEI Z, ZENG Y, LIU P et al. Active deep learning for hyperspectral image classification with uncertainty learning[J]. IEEE Geoscience and Remote Sensing Letters, 19, 1-5(2022).
[27] FANG L Y, LIU G Y, LI S T et al. Hyperspectral image classification with squeeze multibias network[J]. IEEE Transactions on Geoscience and Remote Sensing, 57, 1291-1301(2019).
[28] MENG Z, JIAO L C, LIANG M M et al. A lightweight spectral-spatial convolution module for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 19, 3069202(2022).
[29] ZHANG X M, TIAN Q J, GU X F. An attention based lightweight network for hyperspectral images classification[C], 17, 2251-2254(2022).
Get Citation
Copy Citation Text
Jing LIU, Yinqiao LI, Yi LIU. Active learning-clustering-group convolutions network for hyperspectral images classification[J]. Optics and Precision Engineering, 2024, 32(9): 1395
Category:
Received: Nov. 2, 2023
Accepted: --
Published Online: Jun. 2, 2024
The Author Email: Jing LIU (zyhalj1975@163.com)