• Laser & Optoelectronics Progress
  • Vol. 60, Issue 10, 1010004 (2023)
Shuai Yuan, Yanan Sun*, Weifeng He, and Shikui Tu
Author Affiliations
  • School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
  • show less
    DOI: 10.3788/LOP213289 Cite this Article Set citation alerts
    Shuai Yuan, Yanan Sun, Weifeng He, Shikui Tu. Hyperspectral On-Board Classification Algorithm Based on Multiscale Feature Extraction[J]. Laser & Optoelectronics Progress, 2023, 60(10): 1010004 Copy Citation Text show less
    References

    [1] Ma Y, Wu H P, Wang L Z et al. Remote sensing big data computing: challenges and opportunities[J]. Future Generation Computer Systems, 51, 47-60(2015).

    [2] Alcolea A, Paoletti M E, Haut J M et al. Inference in supervised spectral classifiers for on-board hyperspectral imaging: an overview[J]. Remote Sensing, 12, 534-562(2020).

    [3] Basterretxea K, Martinez-Corral U, Finker R et al. ELM-based hyperspectral imagery processor for onboard real-time classification[C], 43-50(2016).

    [4] Martins L A, Sborz G A M, Viel F et al. An SVM-based hardware accelerator for onboard classification of hyperspectral images[C](2019).

    [5] Haut J M, Bernabé S, Paoletti M E et al. Low-high-power consumption architectures for deep-learning models applied to hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 16, 776-780(2019).

    [6] Makantasis K, Karantzalos K, Doulamis A et al. Deep supervised learning for hyperspectral data classification through convolutional neural networks[C], 4959-4962(2015).

    [7] Guo Q, Peng L. Hyperspectral classification based on 3D convolutional neural network and super pixel segmentation[J]. Acta Optica Sinica, 41, 2210001(2021).

    [8] 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).

    [9] 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).

    [10] Zhi L, Yu X C, Fu Q Y. Hyperspectral imagery spatial-spectral classification combining local binary patterns[J]. Journal of Geomatics Science and Technology, 35, 65-69, 76(2018).

    [11] 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, 43, 480-491(2005).

    [12] Niu Z J, Liu W, Zhao J Y et al. DeepLab-based spatial feature extraction for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 16, 251-255(2019).

    [13] Li T, Leng J B, Kong L Y et al. DCNR: deep cube CNN with random forest for hyperspectral image classification[J]. Multimedia Tools and Applications, 78, 3411-3433(2019).

    [14] Ye Z, Bai L. Hyperspectral image classification based on principal component analysis and local binary patterns[J]. Laser & Optoelectronics Progress, 54, 111006(2017).

    [15] 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).

    [16] Mitchell, Myers, Boyne. A max-min measure for image texture analysis[J]. IEEE Transactions on Computers, C-26, 408-414(1977).

    [17] Zhang X D, Wang T J, Yang Y. Classification of small-sized sample hyperspectral images based on multi-scale residual network[J]. Laser&Optoelectronics Progress, 57, 162801(2020).

    [18] Lazebnik S, Schmid C, Ponce J. Beyond bags of features: spatial pyramid matching for recognizing natural scene categories[C], 2169-2178(2006).

    [19] Sun B C, Feng J S, Saenko K. Return of frustratingly easy domain adaptation[C], 2058-2065(2016).

    [20] Pu H Y, Chen Z, Wang B et al. A novel spatial-spectral similarity measure for dimensionality reduction and classification of hyperspectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 52, 7008-7022(2014).

    [21] Horowitz M. 1.1 Computing’s energy problem (and what we can do about it)[C], 10-14(2014).

    Shuai Yuan, Yanan Sun, Weifeng He, Shikui Tu. Hyperspectral On-Board Classification Algorithm Based on Multiscale Feature Extraction[J]. Laser & Optoelectronics Progress, 2023, 60(10): 1010004
    Download Citation