Hyperspectral Image Classification Algorithm Based on Space-Spectral Weighted Nearest Neighbor

Lei Ji; Xin Zhang; Limei Zhang; Zhang Wen

doi:10.3788/LOP57.061013

[1] Khodadadzadeh M, Li J, Prasad S et al. Fusion of hyperspectral and LiDAR remote sensing data using multiple feature learning[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8, 2971-2983(2015).

[2] Wang L Z, Zhang J B, Liu P et al. Spectral-spatial multi-feature-based deep learning for hyperspectral remote sensing image classification[J]. Soft Computing, 21, 213-221(2017).

[3] Tang Z Q, Fu G Y, Chen J et al. Multiscale segmentation-based sparse coding for hyperspectral image classification[J]. Optics and Precision Engineering, 23, 2708-2714(2015).

[4] Paoletti M E, Haut J M, Fernandez-Beltran R et al. Deep pyramidal residual networks for spectral-spatial hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 57, 740-754(2019).

[5] van Cleemput E, Vanierschot L, Fern ndez-Castilla B et al. The functional characterization of grass- and shrubland ecosystems using hyperspectral remote sensing: trends, accuracy and moderating variables[J]. Remote Sensing of Environment, 209, 747-763(2018).

[6] Cao X Y, Zhou F, Xu L et al. Hyperspectral image classification with Markov random fields and a convolutional neural network[J]. IEEE Transactions on Image Processing, 27, 2354-2367(2018).

[7] Zhao C H, Liu W, Xu Y et al. A spectral-spatial SVM-based multi-layer learning algorithm for hyperspectral image classification[J]. Remote Sensing Letters, 9, 218-227(2018).

[8] 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).

[9] Li F Y, Huo H T, Li J et al. Hyperspectral image classification via multiple-feature-based improved sparse representation[J]. Acta Optica Sinica, 39, 0528004(2019).

[10] Du E Y, Zhang N, Li Y D. Multi classification method of lane arrow markings based on support vector machines with adaptive partitioning coding[J]. Acta Optica Sinica, 38, 1015003(2018).

[11] Liu F, Lu L X, Huang G W et al. Landform image classification based on discrete cosine transformation and deep network[J]. Acta Optica Sinica, 38, 0620001(2018).

[12] 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).

[13] Tan K, Hu J, Li J et al. A novel semi-supervised hyperspectral image classification approach based on spatial neighborhood information and classifier combination[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 105, 19-29(2015).

[14] Huang H, Zheng X L. Hyperspectral image classification with combination of weighted spatial-spectral and KNN[J]. Optics and Precision Engineering, 24, 873-881(2016).

[15] Wang Z J, Yu Z J, Ma K et al. An image filtering algorithm based on adaptive median and gradient inverse weight[J]. Laser & Optoelectronics Progress, 54, 121001(2017).

[16] Chen J Y, Zhan Y W, Cao H Y et al. Adaptive probability filter for removing salt and pepper noises[J]. IET Image Processing, 12, 863-871(2018).

[17] Singh V, Dev R, Dhar N K et al. Adaptive type-2 fuzzy approach for filtering salt and pepper noise in grayscale images[J]. IEEE Transactions on Fuzzy Systems, 26, 3170-3176(2018).

[18] Zhu H L, Liu C, Zhang B et al. Research on laser ultrasonic visual image processing[J]. Chinese Journal of Lasers, 45, 0104004(2018).

[19] Yu J J, Liu J L. Simulation of bioluminescence tomography based on nonconvex L1-2 regularization[J]. Chinese Journal of Lasers, 45, 0407006(2018).