Laser & Optoelectronics Progress, Volume. 59, Issue 24, 2428004(2022)

Hyperspectral Remote Sensing Classification Using Multi-Scale Adaptive Capsule Network

Gen Zhang1,2,3, Xiaohui Ding1,3、*, Ji Yang1,3, and Hua Wang2
Author Affiliations
  • 1Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, Guangdong, China
  • 2School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, Guangdong, China
  • 3Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, Guangdong, China
  • show less
    References(27)

    [1] Feddema J J, Oleson K W, Bonan G B et al. The importance of land-cover change in simulating future climates[J]. Science, 310, 1674-1678(2005).

    [2] Tang H J, Wu W B, Yang P et al. Recent progresses of land use and land cover change(LUCC) models[J]. Acta Geographica Sinica, 64, 456-468(2009).

    [3] Ding X H, Zhang S Q, Li H P et al. A restrictive polymorphic ant colony algorithm for the optimal band selection of hyperspectral remote sensing images[J]. International Journal of Remote Sensing, 41, 1093-1117(2020).

    [4] Hu Y B, Zhang J, Ma Y et al. Hyperspectral coastal wetland classification based on a multiobject convolutional neural network model and decision fusion[J]. IEEE Geoscience and Remote Sensing Letters, 16, 1110-1114(2019).

    [5] Ding X H, Li H P, Zhang S Q. Optimized band selection of hyperspectral remote sensing image based on polymorphic ant colony algorithm[J]. Remote Sensing Technology and Application, 31, 275-284(2016).

    [6] Zhao W D, Li S S, Li A et al. Deep fusion of hyperspectral images and multi-source remote sensing data for classification with convolutional neural network[J]. National Remote Sensing Bulletin, 25, 1489-1502(2021).

    [7] Tang Y, Liu Z J, Yang Y et al. Research on building extraction based on neural network with feature enhancement and ELU activation function[J]. Journal of Geo-Information Science, 23, 692-709(2021).

    [8] Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks[J]. Science, 313, 504-507(2006).

    [9] Li H P, Zhang C, Zhang S Q et al. A hybrid OSVM-OCNN method for crop classification from fine spatial resolution remotely sensed imagery[J]. Remote Sensing, 11, 2370(2019).

    [10] Zhang C, Pan X, Li H P et al. A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 140, 133-144(2018).

    [11] Othman E, Bazi Y, Alajlan N et al. Using convolutional features and a sparse autoencoder for land-use scene classification[J]. International Journal of Remote Sensing, 37, 2149-2167(2016).

    [12] Sharma A, Liu X W, Yang X J et al. A patch-based convolutional neural network for remote sensing image classification[J]. Neural Networks, 95, 19-28(2017).

    [13] Fu S L, Xie C B, Li L et al. PM2.5 concentration identification based on lidar detection[J]. Acta Optica Sinica, 41, 0928001(2021).

    [14] Liu Y, Fu Z Y, Zheng F B. Review on high resolution remote sensing image classification and recognition[J]. Journal of Geo-Information Science, 17, 1080-1091(2015).

    [15] Nong Y J, Wang J J, Zhao X B et al. Spatial relationship detection method of remote sensing objects[J]. Acta Optica Sinica, 41, 1628001(2021).

    [16] Peng X Y, Yang J Q, Wu C H et al. Improvement of dynamic range of laser positioning system based on back propagation neural network[J]. Acta Optica Sinica, 41, 0620001(2021).

    [17] Liu J X, Ban W, Chen Y et al. Multi-dimensional CNN fused algorithm for hyperspectral remote sensing image classification[J]. Chinese Journal of Lasers, 48, 1610003(2021).

    [18] Xiang C Q, Zhang L, Tang Y et al. MS-CapsNet: a novel multi-scale capsule network[J]. IEEE Signal Processing Letters, 25, 1850-1854(2018).

    [19] Sabour S, Frosst N, Hinton G E. Dynamic routing between capsules[C], 3856-3866(2017).

    [21] Nguyen C D T, Dao H H, Huynh M T et al. ResCap: residual capsules network for medical image segmentation[C](2019).

    [23] Chen R L, Jalal M A, Mihaylova L et al. Learning capsules for vehicle logo recognition[C], 565-572(2018).

    [24] Beşer F, Kizrak M A, Bolat B et al. Recognition of sign language using capsule networks[C], 17914316(2018).

    [25] Paoletti M E, Haut J M, Fernandez-Beltran R et al. Capsule networks for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 57, 2145-2160(2019).

    [26] Wang W Y, Li H C, Pan L et al. Hyperspectral image classification based on capsule network[C], 3571-3574(2018).

    [27] Deng F, Pu S L, Chen X H et al. Hyperspectral image classification with capsule network using limited training samples[J]. Sensors, 18, 3153(2018).

    Tools

    Get Citation

    Copy Citation Text

    Gen Zhang, Xiaohui Ding, Ji Yang, Hua Wang. Hyperspectral Remote Sensing Classification Using Multi-Scale Adaptive Capsule Network[J]. Laser & Optoelectronics Progress, 2022, 59(24): 2428004

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Remote Sensing and Sensors

    Received: Sep. 7, 2021

    Accepted: Nov. 2, 2021

    Published Online: Nov. 28, 2022

    The Author Email: Ding Xiaohui (dxh2017@sina.com)

    DOI:10.3788/LOP202259.2428004

    Topics