| Citation: | WANG Cong, ZHAGN Jinyang, ZHANG Lei, et al. Small sample hyperspectral image classification method based on memory association learning[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(3): 549-557. doi: 10.13700/j.bh.1001-5965.2020.0498(in Chinese)
|
Hyperspectral Image (HSI) classification is one of the fundamental applications in remote sensing domain. Due to the expensive cost of manual labeling in HSIs, in real applications, only small labeled samples can be obtained. However, limited samples cannot accurately describe the data distribution and often cause the training of classifiers to be overfitting. To address this problem, we present a small sample hyperspectral image classification method based on memory association learning. First, considering that the unlabeled samples also contain a lot of information related to the data distribution, we construct a memory module based on the labeled samples. Then, according to the feature association among labeled and unlabeled samples, we learn the label distribution of the unlabeled sample with the continuously updated memory module. Finally, we build an unsupervised classifier model and a supervised classifier model, and jointly learn these two models. Extensive experimental results on multiple hyperspectral image classification datasets demonstrate that the proposed method can effectively improve the accuracy of small sample HSI classification.
| [1] |
LANDGREBE D. Hyperspectral image data analysis[J]. IEEE Signal Processing Magazine, 2002, 19(1): 17-28. doi: 10.1109/79.974718
|
| [2] |
BISHOP C A, LIU J G, MASON P J. Hyperspectral remote sensing for mineral exploration in Pulang, Yunnan Province, China[J]. International Journal of Remote Sensing, 2011, 32(9): 2409-2426. doi: 10.1080/01431161003698336
|
| [3] |
ZHANG B, WU D, ZHANG L, et al. Application of hyperspectral remote sensing for environment monitoring in mining areas[J]. Environmental Earth Sciences, 2012, 65(3): 649-658. doi: 10.1007/s12665-011-1112-y
|
| [4] |
ZHANG Y X, DU B, ZHAGN L P, et al. Joint sparse representation and multitask learning for hyperspectral target detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(2): 894-906. doi: 10.1109/TGRS.2016.2616649
|
| [5] |
WANG C, ZHANG L, WEI W, et al. When low rank representation based hyperspectral imagery classification meets segmented stacked denoising auto-encoder based spatial-spectral feature[J]. Remote Sensing, 2018, 10(2): 284. doi: 10.3390/rs10020284
|
| [6] |
PAN E T, MEI X G, WANG Q D, et al. Spectral-spatial classification for hyperspectral image based on a single GRU[J]. Neurocomputing, 2020, 387: 150-160. doi: 10.1016/j.neucom.2020.01.029
|
| [7] |
GUO G D, WANG H, BELL D A, et al. KNN model-based approach in classification[C]//OTM Confederated International Conferences "On the Move to Meaningful Internet Systems". Berlin: Springer, 2003: 986-996.
|
| [8] |
MELGANI F, BRUZZONE L. Classification of hyperspectral remote sensing images with support vector machines[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(8): 1778-1790. doi: 10.1109/TGRS.2004.831865
|
| [9] |
LI W, DU Q. Joint within-class collaborative representation for hyperspectral image classification[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(6): 2200-2208. doi: 10.1109/JSTARS.2014.2306956
|
| [10] |
HU W, HUANG Y Y, LI W, et al. Deep convolutional neural networks for hyperspectral image classification[J]. Journal of Sensors, 2015, 2015: 258619.
|
| [11] |
LIU X F, SUN Q Q, LIU B, et al. Hyperspectral image classification based on convolutional neural network and dimension reduction[C]//2017 Chinese Automation Congress. Piscataway: IEEE Press, 2017: 1686-1690.
|
| [12] |
LEE H, KWON H. Going deeper with contextual CNN for hyperspectral image classification[J]. IEEE Transactions on Image Processing, 2017, 26(10): 4843-4855. doi: 10.1109/TIP.2017.2725580
|
| [13] |
ZHONG Z L, LI J, LUO Z M, et al. Chapman: Spectral-spatial residual network for hyperspectral image classification: A 3-D deep learning framework[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(2): 847-858. doi: 10.1109/TGRS.2017.2755542
|
| [14] |
FANG B, LI Y, ZHAGN H K, et al. Hyperspectral images classification based on dense convolutional networks with spectral-wise attention mechanism[J]. Remote Sensing, 2019, 11(2): 159. doi: 10.3390/rs11020159
|
| [15] |
PAOLETTI M E, HAUT J M, BELTRAN R F, et al. Capsule networks for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(4): 2145-2160. doi: 10.1109/TGRS.2018.2871782
|
| [16] |
SAMIAPPAN S, MOORHEAD R J. Semi-supervised co-training and active learning framework for hyperspectral image classification[C]//IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Piscataway: IEEE Press, 2015: 401-404.
|
| [17] |
RASMUS A, VALPOLA H, HONKALA M, et al. Semi-supervised learning with ladder networks[EB/OL]. (2015-07-09)[2020-08-01]. https://arxiv.org/abs/1507.02672.
|
| [18] |
WEI W, ZHAGN L, LI Y, et al. Intraclass similarity structure representation-based hyperspectral imagery classification with few samples[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13: 1045-1054. doi: 10.1109/JSTARS.2020.2977655
|
| [19] |
LI W, WU G D, ZHANG F, et al. Hyperspectral image classification using deep pixel-pair features[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(2): 844-853. doi: 10.1109/TGRS.2016.2616355
|
| [20] |
LIU B, YU X C, ZHAGN P Q, et al. A semi-supervised convolutional neural network for hyperspectral image classification[J]. Remote Sensing Letters, 2017, 8(9): 839-848. doi: 10.1080/2150704X.2017.1331053
|
| [21] |
PONTIUS R G, MILLONES M. Death to Kappa: Birth of quantity disagreement and allocation disagreement for accuracy assessment[J]. International Journal of Remote Sensing, 2011, 32(15): 4407-4429. doi: 10.1080/01431161.2011.552923
|
Figures(5) / Tables(12)
Copyright © Journal of Beijing University of Aeronautics and Astronautics
Address: Editorial Department of Journal of Beijing University of Aeronautics and Astronautics, 37 Xueyuan Road, Haidian District, Beijing Post Code: 100191 Email: jbuaa@buaa.edu.cn
Supported by:
Beijing Renhe Information Technology Co., Ltd.
DownLoad:
