A Deep few-shot learning algorithm for hyperspectral image classification

doi:10.11947/j.AGCS.2020.20190486

Abstract

Abstract: For hyperspectral image classification problem of small sample, this paper proposes a depth of less sample learning algorithm, this algorithm through the simulation of the small sample classification in the process of training is to train the depth 3D convolution neural network feature extraction, the extraction of characteristic with smaller class span and large spacing between classes, more suitable for small sample classification problem, and can be used for different hyperspectral data, has better generalization ability. The trained model is used to extract the features of the target data set, and then the nearest neighbor classifier and support vector machine classifier are combined for supervised classification. Three groups of hyperspectral image data of Pavia university, Indian Pines and Salinas were used in the classification experiment. The experimental results showed that the algorithm could achieve a better classification accuracy than the traditional semi-supervised classification method under the condition of fewer training samples (only 5 marked samples were selected for each type of feature as training samples).

Key words: hyperspectral image classification, deep less sample learning, deep three-dimensional convolutional network, nearest neighbor classification

CLC Number:

P237

LIU Bing, ZUO Xibing, TAN Xiong, YU Anzhu, GUO Wenyue. A Deep few-shot learning algorithm for hyperspectral image classification[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(10): 1331-1342.

References

[1] GHAMISI P, PLAZA J, CHEN Yushi, et al. Advanced spectral classifiers for hyperspectral images:a review[J]. IEEE Geoscience and Remote Sensing Magazine, 2017, 5(1):8-32.
[2] RODARMEL C, SHAN Jie. Principal component analysis for hyperspectral image classification[J]. Surveying and Land Information Science, 2002, 62(2):115-123.
[3] VILLA A, BENEDIKTSSON J A, CHANUSSOT J, et al. Hyperspectral image classification with independent component discriminant analysis[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(12):4865-4876.
[4] BENEDIKTSSON J A, PALMASON J A, SVEINSSON J R. Classification of hyperspectral data from urban areas based on extended morphological profiles[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(3):480-491.
[5] TARABALKA Y, FAUVEL M, CHANUSSOT J, et al. SVM-and MRF-based method for accurate classification of hyperspectral images[J]. IEEE Geoscience and Remote Sensing Letters, 2010, 7(4):736-740.
[6] LI Wei, CHEN Chen, SU Hongjun, et al. Local binary patterns and extreme learning machine for hyperspectral imagery classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(7):3681-3693.
[7] JIA Sen, HU Jie, XIE Yao, et al. Gabor cube selection based multitask joint sparse representation for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(6):3174-3187.
[8] SUN Kang, GENG Xiurui, CHEN Jinyong, et al. A robust and efficient band selection method using graph representation for hyperspectral imagery[J]. International Journal of Remote Sensing, 2016, 37(20):4874-4889.
[9] JOACHIMS T. Transductive inference for text classification using support vector machines[C]//Proceedings of the Sixteenth International Conference on Machine Learning. Bled, Slovenia:Morgan Kaufmann Publishers Inc., 1999:200-209.
[10] ANDO R K, ZHANG Tong. Two-view feature generation model for semi-supervised learning[C]//Proceedings of the 24th International Conference on Machine Learning. Corvalis:ACM, 2007:25-32.
[11] SUN Shujin, ZHONG Ping, XIAO Huaitie, et al. Active learning with Gaussian process classifier for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(4):1746-1760.
[12] CHEN Yushi, LIN Zhouhan, ZHAO Xing, et al. Deep learning-based classification of hyperspectral data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(6):2094-2107.
[13] CHEN Yushi, ZHAO Xing, JIA Xiuping. Spectral-spatial classification of hyperspectral data based on deep belief network[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(6):2381-2392.
[14] HU Yangyu, HUANG Wei, ZHANG Fan, et al. Deep convolutional neural networks for hyperspectral image classification[J]. Journal of Sensors, 2015, 2015:258619.
[15] MOU Lichao, GHAMISI P, ZHU Xiaoxiang. Deep recurrent neural networks for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(7):3639-3655.
[16] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[J/OL]. (2014-09-04). https://arxiv.org/abs/1409.1556.
[17] 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.
[18] LI Wei, WU Guodong, ZHANG Fan, et al. Hyperspectral image classification using deep pixel-pair features[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(2):844-853.
[19] LIU Bing, YU Xuchu, ZHANG Pengqiang, et al. A semi-supervised convolutional neural network for hyperspectral image classification[J]. Remote Sensing Letters, 2017, 8(9):839-848.
[20] MEI Shaohui, JI Jingyu, HOU Junhui, et al. Learning sensor-specific spatial-spectral features of hyperspectral images via convolutional neural networks[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(8):4520-4533.
[21] 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, 48(1):53-63. DOI:10.11947/j.AGCS.2019.20170578.
[22] 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, UT, USA:IEEE, 2018:1199-1208.
[23] VINYALS O, BLUNDELL C, LILLICRAP T, et al. Matching networks for one shot learning[C]//Proceedings of the 30th International Conference on Neural Information Processing Systems. Barcelona:Curran Associates Inc, 2016:3637-3645.
[24] RAVI S, LAROCHELLE H. Optimization as a model for few-shot learning[J]. International Conference on Learning Representations, 2017, 87(17):4-15.
[25] YOKOYA N, IWASAKI A. Airborne hyperspectral data over Chikusei[R]. Tokyo:The University of Tokyo, 2016.
[26] WANG Liguo, HAO Siyuan, WANG Qunming, et al. Semi-supervised classification for hyperspectral imagery based on spatial-spectral Label Propagation[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 97:123-137.
[27] TAN Kun, HU Jun, LI Jun, et al. A novel semi-supervised hyperspectral image classification approach based on spatial neighborhood information and classifier combination[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 105:19-29.
[28] DÓPIDO I, LI Jun, MARPU P R, et al. Semisupervised self-learning for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(7):4032-4044.
[29] 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.
[30] MAKANTASIS K, KARANTZALOS K, DOULAMIS A, et al. Deep supervised learning for hyperspectral data classification through convolutional neural networks[C]//Proceedings of 2015 IEEE International Geoscience and Remote Sensing Symposium. Milan, Italy:IEEE, 2015:4959-4962.
[31] DAI Yuchao, ZHANG Jing, HE Mingyi, et al. Salient object detection from multi-spectral remote sensing images with deep residual network[J]. Journal of Geodesy and Geoinformation Science, 2019, 2(2):101-110. DOI:10.11947/j.JGGS.2019.0211.
[32] LIN Wenjie, LI Yu, ZHAO Quanhua. High-resolution remote sensing image segmentation using minimum spanning tree tessellation and RHMRF-FCM algorithm[J]. Journal of Geodesy and Geoinformation Science, 2020, 3(1):52-63. DOI:10.11947/j.JGGS.2020.0106.