Abstract
Hyperspectral image (HSI) classification plays an important role in a wide range of remote sensing applications in military and civilian fields. During past decades, significant efforts have been made on developing datasets and introducing novel approaches to promote HSI classification, such that promising classification performance has been achieved. However, existing datasets generally pose following issues, including the limited categories and annotated samples, the lack of sample diversity, as well as the low spatial resolution. These limitations severely restrict the development and evaluation of data-driven models, especially deep neural network-based ones. In recent years, advances in imaging spectroscopy provide us the opportunity to obtain the hyperspectral image data with high spectral and spatial resolution, therefore, in this paper, we contribute a large-scale benchmark dataset for conducting hyperspectral image classification to address issues raised by existing datasets, noted as ShanDongFeiCheng (SDFC). The proposed SDFC is characterized by (1) The large-scale annotated samples with diverse categories; (2) The high spatial resolution; and (3) The high intra-class variance yet relatively low inter-class variance, making the HSI classification task much more challenging on it. We evaluated 10 classic traditional and deep neural network-based models on SDFC, of which the results can be regarded as useful baselines for further experiments. Moreover, given the state-of-the-art performance of SpectralNet, we selected it as the representation method, and evaluated it across datasets to analyze the difference effects on the classification model induced by different datasets. The comprehensive review and analysis of the representative classification models on both existing and proposed datasets demonstrate the advantages and challenges of our proposed dataset, and provide promising perspectives for future HSI classification studies.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data Availability
Availability of data and materials The KSC, Indian Pines, Salinas Scene, Pavia University, Pavia Center, and Botswana datasets are available online at http://www.ehu.eus/ccwintco/index.php?title=Hyperspectral_Remote_Sensing_Scenes. If you want to use the SDFC dataset, please contact the first author or corresponding author.
References
Abdulsamad, T., Chen, F., Xue, Y., Wang, Y., Zeng, D.: Hyperspectral image classification based on spectral and spatial information using resnet with channel attention. Opt. Quant. Electron. 53(3), 159 (2021). https://doi.org/10.1007/s11082-020-02671-4
Bandara, W.G.C., Valanarasu, J.M.J., Patel, V.M.: Hyperspectral pansharpening based on improved deep image prior and residual reconstruction. IEEE Trans. Geosci. Remote Sens. 60, 1–16 (2022). https://doi.org/10.1109/TGRS.2021.3139292
Cen, Y., Zhang, L., Zhang, X., Wang, Y., Qi, W., Tang, S., Zhang, P.: Aerial hyperspectral remote sensing classification dataset of xiongan new area(matiwan village). J. Remote Sens. 24(11), 1299–1306 (2020). https://doi.org/10.11834/jrs.20209065
Chakraborty, T., Trehan, U.: Spectralnet: Exploring spatial-spectral waveletcnn for hyperspectral image classification. CoRR abs/2104.00341 (2021) arXiv:2104.00341
Ding, Y., Zhao, X., Zhang, Z., Cai, W., Yang, N.: Multiscale graph sample and aggregate network with context-aware learning for hyperspectral image classification. Sens. IEEE J. Sel. Top. Appl. Earth Obs. Remote. 14, 4561–4572 (2021). https://doi.org/10.1109/JSTARS.2021.3074469
Ding, Y., Zhao, X., Zhang, Z., Cai, W., Yang, N.: Graph sample and aggregate-attention network for hyperspectral image classification. IEEE Geosci. Remote Sens. Lett. 19, 1–5 (2022). https://doi.org/10.1109/LGRS.2021.3062944
Ding, Y., Zhao, X., Zhang, Z., Cai, W., Yang, N., Zhan, Y.: Semi-supervised locality preserving dense graph neural network with ARMA filters and context-aware learning for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 60, 1–12 (2022). https://doi.org/10.1109/TGRS.2021.3100578
Ding, Y., Zhang, Z., Zhao, X., Cai, Y., Li, S., Deng, B., Cai, W.: Self-supervised locality preserving low-pass graph convolutional embedding for large-scale hyperspectral image clustering. IEEE Trans. Geosci. Remote Sens. 60, 1–16 (2022). https://doi.org/10.1109/TGRS.2022.3198842
Hang, R., Li, Z., Liu, Q., Ghamisi, P., Bhattacharyya, S.S.: Hyperspectral image classification with attention-aided CNNS. IEEE Trans. Geosci. Remote Sens. 59(3), 2281–2293 (2021). https://doi.org/10.1109/TGRS.2020.3007921
He, M., Li, B., Chen, H.: Multi-scale 3d deep convolutional neural network for hyperspectral image classification. In: 2017 IEEE International Conference on Image Processing, ICIP 2017, Beijing, China, September 17-20, 2017, pp. 3904–3908 (2017). https://doi.org/10.1109/ICIP.2017.8297014
He, L., Li, J., Liu, C., Li, S.: Recent advances on spectral-spatial hyperspectral image classification: An overview and new guidelines. IEEE Trans. Geosci. Remote Sens. 56(3), 1579–1597 (2018). https://doi.org/10.1109/TGRS.2017.2765364
He, J., Zhao, L., Yang, H., Zhang, M., Li, W.: HSI-BERT: hyperspectral image classification using the bidirectional encoder representation from transformers. IEEE Trans. Geosci. Remote Sens. 58(1), 165–178 (2020). https://doi.org/10.1109/TGRS.2019.2934760
Hladik, C., Schalles, J., Alber, M.: Salt marsh elevation and habitat mapping using hyperspectral and lidar data. Remote Sens. Environ. 139, 318–330 (2013). https://doi.org/10.1016/j.rse.2013.08.003
Hu, X., Zhong, Y., Luo, C., Wang, X.: Whu-hi: Uav-borne hyperspectral with high spatial resolution (H2) benchmark datasets for hyperspectral image classification. CoRR abs/2012.13920 (2020) arXiv:2012.13920
Huang, K., Ren, C., Liu, H., Lai, Z., Yu, Y., Dai, D.: Hyperspectral image classification via discriminative convolutional neural network with an improved triplet loss. Pattern Recognit. 112, 107744 (2021). https://doi.org/10.1016/j.patcog.2020.107744
Lee, H., Kwon, H.: Contextual deep CNN based hyperspectral classification. In: 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 3322–3325 (2016). https://doi.org/10.1109/IGARSS.2016.7729859
Liu, B., Yu, X., Zhang, P., Yu, A., Fu, Q., Wei, X.: Supervised deep feature extraction for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 56(4), 1909–1921 (2018). https://doi.org/10.1109/TGRS.2017.2769673
Mou, L., Ghamisi, P., Zhu, X.: Unsupervised spectral-spatial feature learning via deep residual conv-deconv network for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 56(1), 391–406 (2018). https://doi.org/10.1109/TGRS.2017.2748160
Palash, U.M., Al, M.M., Ibn, A.M., Ali, H.M.: Information-theoretic feature selection with segmentation-based folded principal component analysis (pca) for hyperspectral image classification. Int. J. Remote Sens. 42(1), 286–321 (2021). https://doi.org/10.1080/01431161.2020.1807650
Paoletti, M., Haut, J.M., Plaza, J., Plaza, A.: Deep learning classifiers for hyperspectral imaging: A review. ISPRS J. Photogramm. Remote. Sens. 158, 279–317 (2019). https://doi.org/10.1016/j.isprsjprs.2019.09.006
Qing, Y., Liu, W., Feng, L., Gao, W.: Improved transformer net for hyperspectral image classification. Remote. Sens. 13(11), 2216 (2021). https://doi.org/10.3390/rs13112216
Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: Hybridsn: Exploring 3-d-2-d CNN feature hierarchy for hyperspectral image classification. IEEE Geosci. Remote Sens. Lett. 17(2), 277–281 (2020). https://doi.org/10.1109/LGRS.2019.2918719
Roy, S.K., Manna, S., Song, T., Bruzzone, L.: Attention-based adaptive spectral-spatial kernel resnet for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 59(9), 7831–7843 (2021). https://doi.org/10.1109/TGRS.2020.3043267
Samat, A., Gamba, P., Abuduwaili, J., Liu, S., Miao, Z.: Geodesic flow kernel support vector machine for hyperspectral image classification by unsupervised subspace feature transfer. Remote. Sens. 8(3), 234 (2016). https://doi.org/10.3390/rs8030234
Wang, W., Dou, S., Jiang, Z., Sun, L.: A fast dense spectral-spatial convolution network framework for hyperspectral images classification. Remote. Sens. 10(7), 1068 (2018). https://doi.org/10.3390/rs10071068
Wei, Y., Zhou, Y.: Spatial-aware network for hyperspectral image classification. Remote. Sens. 13(16), 3232 (2021). https://doi.org/10.3390/rs13163232
Xu, Y., Du, B., Zhang, F., Zhang, L.: Hyperspectral image classification via a random patches network. ISPRS J. Photogramm. Remote. Sens. 142(8), 344–357 (2018). https://doi.org/10.1016/j.isprsjprs.2018.05.014
Xu, Y., Zhang, L., Du, B., Zhang, F.: Spectral-spatial unified networks for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 56(10), 5893–5909 (2018). https://doi.org/10.1109/TGRS.2018.2827407
Xu, Y., Du, B., Zhang, L.: Beyond the patchwise classification: Spectral-spatial fully convolutional networks for hyperspectral image classification. IEEE Trans. Big Data 6(3), 492–506 (2020). https://doi.org/10.1109/TBDATA.2019.2923243
Xue, Z., Zhang, M., Liu, Y., Du, P.: Attention-based second-order pooling network for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 59(11), 9600–9615 (2021). https://doi.org/10.1109/TGRS.2020.3048128
Yan, H., Yu, M., Xia, J., Zhu, L., Zhang, T., Zhu, Z., Sun, G.: Diverse region-based CNN for tongue squamous cell carcinoma classification with raman spectroscopy. IEEE Access 8, 127313–127328 (2020). https://doi.org/10.1109/ACCESS.2020.3006567
Zheng, Z., Zhong, Y., Ma, A., Zhang, L.: FPGA: fast patch-free global learning framework for fully end-to-end hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 58(8), 5612–5626 (2020). https://doi.org/10.1109/TGRS.2020.2967821
Zheng, H., Cao, Y., Sun, M., Guo, G., Meng, J., Guo, X., Jiang, Y.: Mixed structure with 3d multi-shortcut-link networks for hyperspectral image classification. Remote. Sens. 14(5), 1230 (2022). https://doi.org/10.3390/rs14051230
Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral-spatial residual network for hyperspectral image classification: A 3-d deep learning framework. IEEE Trans. Geosci. Remote Sens. 56(2), 847–858 (2018). https://doi.org/10.1109/TGRS.2017.2755542
Zhu, Q., Deng, W., Zheng, Z., Zhong, Y., Guan, Q., Lin, W., Zhang, L., Li, D.: A spectral-spatial-dependent global learning framework for insufficient and imbalanced hyperspectral image classification. IEEE Trans. Cybern. 52(11), 11709–11723 (2022). https://doi.org/10.1109/TCYB.2021.3070577
Funding
This work was supported in part by the National Natural Science Foundation of China (No.61572307).
Author information
Authors and Affiliations
Contributions
LS and JZ designed the dataset and experiments. All authors wrote the article. DZ and JZ guided the research. All authors have read and agreed to the published version of the manuscript.
Corresponding author
Ethics declarations
Ethical Approval
Not applicable.
Conflict of interest
The authors declare that they have no conflict of interest
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Sun, L., Zhang, J., Li, J. et al. SDFC dataset: a large-scale benchmark dataset for hyperspectral image classification. Opt Quant Electron 55, 173 (2023). https://doi.org/10.1007/s11082-022-04399-9
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11082-022-04399-9