Hyperspectral Image Classification Based on Dilated Convolutional Attention Neural Network

Xiangdong Zhang; Tengjun Wang; Shaojun Zhu; Yun Yang

doi:10.3788/AOS202141.0310001

[1] Adam E, Mutanga O, Rugege D. Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review[J]. Wetlands Ecology and Management, 18, 281-296(2010). http://www.bioone.org/servlet/linkout?suffix=i1551-5036-29-4-958-Adam1&dbid=16&doi=10.2112%2FJCOASTRES-D-12-00170.1&key=10.1007%2Fs11273-009-9169-z

[2] Liang L, Di L P, Zhang L P et al. Estimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method[J]. Remote Sensing of Environment, 165, 123-134(2015). http://www.sciencedirect.com/science/article/pii/S0034425715001741

[3] Ershat Able, Baidengsha Maimaitiaili, Mamat Sawut et al. Combined estimation of chlorophyll content in cotton canopy based on hyperspectral parameters and back propagation neural network[J]. Acta Optica Sinica, 39, 0930003(2019).

[4] Yang X G, Yu Y. Estimating soil salinity under various moisture conditions: An experimental study[J]. IEEE Transactions on Geoscience and Remote Sensing, 55, 2525-2533(2017).

[5] Govender M, Chetty K, Bulcock H. A review of hyperspectral remote sensing and its application in vegetation and water resource studies[J]. Water SA, 33, 145-151(2009). http://www.oalib.com/paper/1350213

[6] Yokoya N, Chan J, Segl K. Potential of resolution-enhanced hyperspectral data for mineral mapping using simulated EnMAP and sentinel-2 images[J]. Remote Sensing, 8, 172(2016).

[7] Du P J, Xia J S, Xue Z H et al. Review of hyperspectral remote sensing image classification[J]. Journal of Remote Sensing, 20, 236-256(2016).

[8] Farrell M D, Mersereau R M. On the impact of PCA dimension reduction for hyperspectral detection of difficult targets[J]. IEEE Geoscience and Remote Sensing Letters, 2, 192-195(2005). http://ieeexplore.ieee.org/document/1420303/

[9] Wang J, Chang C I. Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis[J]. IEEE Transactions on Geoscience and Remote Sensing, 44, 1586-1600(2006). http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1634722

[10] 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).

[11] 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, 7, 736-740(2010).

[12] Ma L, Crawford M M, Tian J W. Local manifold learning-based k-nearest-neighbor for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 48, 4099-4109(2010).

[13] Wu F Y, Wang X, Ding J W et al. Improved cascade forest deep learning model for hyperspectral imagery classification[J]. Journal of Remote Sensing, 24, 439-453(2020).

[14] Makantasis K, Karantzalos K, Doulamis A et al. Deep supervised learning for hyperspectral data classification through convolutional neural networks[C]∥2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July 26-31, 2015, Mila, 4959-4962(2015).

[15] Li Z Q, Zhu R F, Gao F et al. Hyperspectral remote sensing image classification based on three-dimensional convolution neural network combined with conditional random field optimization[J]. Acta Optica Sinica, 38, 0828001(2018).

[16] 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). http://ieeexplore.ieee.org/document/8736016

[17] Yan M J, Su X Y. Hyperspectral image classification based on three-dimensional dilated convolutional residual neural network[J]. Acta Optica Sinica, 40, 1628002(2020).

[18] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA., 7132-7141(2018).

[19] Woo S, Park J, Lee J Y et al[M]. CBAM: convolutional block attention module, 3-19(2018).

[20] Ji S W, Xu W, Yang M et al. 3D convolutional neural networks for human action recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35, 221-231(2013).

[21] Yu F. -04-30)[2020-07-06]. https:∥arxiv., org/abs/1511, 07122(2016).

[22] Szegedy C, Liu W, Jia Y Q et al. Going deeper with convolutions[C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA., 1-9(2015).