高光谱影像少样例分类的无监督元学习方法

doi:10.11947/j.AGCS.2023.20220410

摘要/Abstract

摘要： 针对高光谱影像少样例分类问题,本文提出了一种无监督元学习方法。该方法能够利用无标记样本进行无监督元学习,在显著减少深度学习模型对大量标记样本依赖的同时有效提高高光谱影像少样例分类精度。首先,基于自监督学习思想利用主成分分析和数据增强方法构造同一样本的不同增强特征,以形成大量不同的任务并对模型进行元训练。然后,分别利用目标影像中随机选择的少量标记样本和全部样本对模型进行微调和分类测试,评估模型在少样例条件下的分类性能,并在微调和分类过程中引入投票策略进一步提高分类精度。本文方法能够在零标记样本的条件下对模型进行充分的无监督元训练,突破了监督元学习方法需要大量源标记样本的瓶颈和限制。3组公开数据集上的试验结果表明,本文方法在少样例条件下能够获得较现有方法更为优异的分类结果。

关键词: 高光谱影像分类, 少样例分类, 无监督元学习, 深度学习, 投票策略

Abstract: Aiming at the problem of hyperspectral images (HSIs) few-shot classification, an unsupervised meta learning method is proposed in this paper. This method can utilize unlabeled samples for unsupervised meta learning, significantly reducing the dependence on a large number of labeled samples while effectively improving the accuracy of HSIs few-shot classification. Firstly, based on the idea of self-supervised learning, multiple different augmentation features of the same sample are generated through principal component analysis and data augmentation methods, to form a large number of different tasks and perform meta-training on the designed model. Then, a few labeled samples randomly selected from target HSIs are used to fine-tune the model, and all labeled samples are used for classification test to evaluate the few-shot classification performance of the model. Moreover, the voting strategy is introduced in the fine-tuning and classification phase, to further improve classification accuracy. The proposed method can perform sufficient unsupervised meta-training under the condition of zero labeled sample, breaking through the bottleneck and limitation of the supervised meta learning methods requiring a large number of source labeled samples. Experiments on three public datasets show that the proposed method can obtain better classification results than existing advanced methods in the few-shot scenario.

Key words: hyperspectral images classification, few-shot classification, unsupervised meta learning, deep learning, voting strategy

中图分类号:

P237

高奎亮, 刘冰, 余岸竹, 徐佰祺, 胡伟, 胡家玮. 高光谱影像少样例分类的无监督元学习方法[J]. 测绘学报, 2023, 52(11): 1941-1952.

GAO Kuiliang, LIU Bing, YU Anzhu, XU Baiqi, HU Wei, HU Jiawei. An unsupervised meta learning method for hyperspectral images few-shot classification[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(11): 1941-1952.

参考文献

[1] ZHANG Liangpei, ZHANG Lefei, DU Bo. Deep learning for remote sensing data:a technical tutorial on the state of the art[J]. IEEE Geoscience and Remote Sensing Magazine, 2016, 4(2):22-40.
[2] 张号逵, 李映, 姜晔楠. 深度学习在高光谱图像分类领域的研究现状与展望[J]. 自动化学报, 2018, 44(6):961-977. ZHANG Haokui, LI Ying, JIANG Yenan. Deep learning for hyperspectral imagery classification:the state of the art and prospects[J]. Acta Automatica Sinica, 2018, 44(6):961-977.
[3] PAOLETTI M E, HAUT J M, PLAZA J, et al. Deep learning classifiers for hyperspectral imaging:a review[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 158(8):279-317.
[4] 刘冰, 余旭初, 张鹏强, 等. 联合空-谱信息的高光谱影像深度三维卷积网络分类[J]. 测绘学报, 2019, 48(1):53-63. DOI:10.11947/j.AGCS.2019.20170578. LIU Bing, YU Xuchu, ZHANG Pengqiang, et al. Deep 3D convolutional network combined with spatial-spectral features for hyperspectral image classification[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(1):53-63. DOI:10.11947/j.AGCS.2019.20170578.
[5] 马晓瑞.基于深度学习的高光谱影像分类方法研究[D]. 大连:大连理工大学,2017. MA Xiaorui. Research on hyperspectral image classification method based on deep learning[D].Dalian:Dalian University of Technology,2017.
[6] YUE Jun, ZHAO Wenzhi, MAO Shanjun, et al. Spectral-spatial classification of hyperspectral images using deep convolutional neural networks[J]. Remote Sensing Letters, 2015, 6(6):468-477.
[7] CHEN Yushi, JIANG Hanlu, LI Chunyang, et al. Deep feature extraction and classification of hyperspectral images based on convolutional neural networks[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(10):6232-6251.
[8] ZHOU Feng, HANG Renlong, LI Juan,et al. Spectral-spatial correlation exploration for hyperspectral image classification via self-mutual attention network[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19:1-5.
[9] ZHI Lu, YU Xuchu, LIU Bing, et al. A dense convolutional neural network for hyperspectral image classification[J]. Remote Sensing Letters, 2019, 10(1):59-66.
[10] LIU Bing, YU Xuchu, ZHANG Pengqiang, et al. Supervised deep feature extraction for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(4):1909-1921.
[11] ROY S K, HAUT J M, PAOLETTI M E, et al. Generative adversarial minority oversampling for spectral-spatial hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60:1-15.
[12] 高奎亮, 余旭初, 张鹏强, 等. 利用胶囊网络实现高光谱影像空谱联合分类[J]. 武汉大学学报(信息科学版), 2022, 47(3):428-437. GAO Kuiliang, YU Xuchu, ZHANG Pengqiang, et al. Hyperspectral image spatial-spectral classification using capsule network based method[J]. Geomatics and Information Science of Wuhan University, 2022, 47(3):428-437.
[13] PAOLETTI M E, MORENO-ÁLVAREZ S, HAUT J M. Multiple attention-guided capsule networks for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60:1-20.
[14] 高奎亮, 余旭初, 宋治杭, 等. 联合空谱信息的高光谱影像深度胶囊网络分类[J]. 遥感学报, 2021, 25(6):1257-1269. GAO Kuiliang, YU Xuchu, SONG Zhihang, et al. Deep capsule network combined with spatial-spectral information for hyperspectral image classification[J]. Journal of Remote Sensing, 2021, 25(6):1257-1269.
[15] YANG Bin, HU Shunshi, GUO Qiandong, et al. Multisource domain transfer learning based on spectral projections for hyperspectral image classification[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15:3730-3739.
[16] 王立国, 马骏宇, 李阳. 联合多种空间信息的高光谱半监督分类方法[J]. 哈尔滨工程大学学报, 2021, 42(2):280-285. WANG Liguo, MA Junyu, LI Yang. Hyperspectral semi-supervised classification algorithm considering multiple spatial information[J]. Journal of Harbin Engineering University, 2021, 42(2):280-285.
[17] 田彦平, 陶超, 邹峥嵘, 等. 主动学习与图的半监督相结合的高光谱影像分类[J]. 测绘学报, 2015, 44(8):919-926. TIAN Yanping, TAO Chao, ZOU Zhengrong, et al. Semi-supervised graph-based hyperspectral image classification with active learning[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(8):919-926.
[18] 左溪冰, 刘冰, 余旭初, 等. 高光谱影像小样本分类的图卷积网络方法[J]. 测绘学报, 2021, 50(10):1358-1369. DOI:10.11947/j.AGCS.2021.20200155. ZUO Xibing, LIU Bing, YU Xuchu, et al. Graph convolutional network method for small sample classification of hyperspectral images[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(10):1358-1369. DOI:10.11947/j.AGCS.2021.20200155.
[19] MEI Shaohui, JI Jingyu, GENG Yunhao, et al. Unsupervised spatial-spectral feature learning by 3D convolutional autoencoder for hyperspectral classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(9):6808-6820.
[20] YU Chunyan, LIU Caiyu, SONG Meiping, et al. Unsupervised domain adaptation with content-wise alignment for hyperspectral imagery classification[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19:1-5.
[21] GUAN Peiyan, LAM E Y. Cross-domain contrastive learning for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60:1-13.
[22] GAO Kuiliang, GUO Wenyue, YU Xuchu, et al. Deep induction network for small samples classification of hyperspectral images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13:3462-3477.
[23] 刘冰, 左溪冰, 谭熊, 等. 高光谱影像分类的深度少样例学习方法[J]. 测绘学报, 2020, 49(10):1331-1342. DOI:10.11947/j.AGCS.2020.20190486. LIU Bing, ZUO Xibing, TAN Xiong, et al. A Deep few-shot learning algorithm for hyperspectral image classification[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(10):1331-1342. DOI:10.11947/j.AGCS.2020.20190486.
[24] FINN C, ABBEEL P, LEVINE S. Model-agnostic meta-learning for fast adaptation of deep networks[C]//Proceedings of the 34th International Conference on Machine Learning.Sydney:ACM Press, 2017:1126-1135.
[25] LIU Bing, YU Xuchu, YU Anzhu, et al. Deep few-shot learning for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(4):2290-2304.
[26] GAO Kuiliang, LIU Bing, YU Xuchu, et al. Deep relation network for hyperspectral image few-shot classification[J]. Remote Sensing, 2020, 12(6):923.
[27] SUNG F, YANG Yongxin, ZHANG Li, et al. Learning to compare:relation network for few-shot learning[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Salt Lake City:IEEE, 2018:1199-1208.
[28] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Las Vegas:IEEE, 2016:770-778.
[29] ZHONG Yanfei, HU Xin, LUO Chang, et al. WHU-Hi:UAV-borne hyperspectral with high spatial resolution (H2) benchmark datasets and classifier for precisecropidentification based on deep convolutional neural network with CRF[J]. Remote Sensing of Environment, 2020, 250:112012.
[30] LIU Bing, GAO Kuiliang, YU Anzhu, et al. Semisupervised graph convolutional network for hyperspectral image classification[J]. Journal of Applied Remote Sensing, 2020, 14(2):026516.