[1] Zhang N N. On the cultural value of China ancient city wall and the present situation of cloud exhibition[J]. Popular Standardization, 131-133(2021).

[2] Bai Y, Zhang Z J, Liu P H et al. Disease classification of the ancient brick in ancient city wall[J]. Shanxi Architecture, 46, 30-32(2020).

[3] Zhu C H, Zhou Y Q. Pathologies investigation and assessment method of an ancient City Wall in Ming Dynasty[J]. Journal of Natural Disasters, 28, 60-73(2019).

[4] Chen G Q, Li L, Li G M et al. Failure modes classification and failure mechanism research of ancient city wall[J]. Environmental Earth Sciences, 76, 810(2017).

[5] Feng B, Hu Y, Hou M. A method for monitoring bulge of ancient city wall after repair[J]. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 46, 221-224(2021).

[6] Zhou W. Application of ground penetrating radar method in detection of diseases in ancient city walls[J]. Intelligent City, 4, 25-26(2018).

[7] Ling X, Wu M L, Liao Y et al. Nondestructive techniques in the research and preservation of cultural relics[J]. Spectroscopy and Spectral Analysis, 38, 2026-2031(2018).

[8] Wang X P, Zhao H X, Li Q H et al. Relevant fundamental research of colored artworks by multispectral imaging technology[J]. Acta Optica Sinica, 35, 1030003(2015).

[9] Nuriel O, Benaim S, Wolf L. Permuted AdaIN: reducing the bias towards global statistics in image classification[C], 9477-9486(2021).

[10] Zhu S Y, Jin Y, Wu Q Y et al. Hybrid-convolution-based reconstruction for limited-view emission spectrum tomography[J]. Acta Optica Sinica, 42, 1315002(2022).

[11] Li Y P, Dai X J, Wang M et al. Study on rapid spectral reappearing and hyperspectral classification of invisible writing[J]. Spectroscopy and Spectral Analysis, 41, 3524-3531(2021).

[12] Chandra M A, Bedi S S. Survey on SVM and their application in image[J]. International Journal of Information Technology, 13, 1-11(2021).

[13] Guo Y H, Cao H, Han S M et al. Spectral-spatial hyperspectral image classification with K-nearest neighbor and guided filter[J]. IEEE Access, 6, 18582-18591(2018).

[14] Cao X H, Li R J, Ge Y M et al. Densely connected deep random forest for hyperspectral imagery classification[J]. International Journal of Remote Sensing, 40, 3606-3622(2019).

[15] Guo Y H, Yin X J, Zhao X C et al. Hyperspectral image classification with SVM and guided filter[J]. EURASIP Journal on Wireless Communications and Networking, 1-9(2019).

[16] Xie C Q, Yang C, He Y. Hyperspectral imaging for classification of healthy and gray mold diseased tomato leaves with different infection severities[J]. Computers and Electronics in Agriculture, 135, 154-162(2017).

[17] Cao C P, Wang H Q, Wang K et al. Intelligent evaluation method of grottoes surface weathering based on multispectral imaging and random forest algorithm[J]. Acta Optica Sinica, 40, 2230001(2020).

[18] Cao X Y, Yao J, Xu Z B et al. Hyperspectral image classification with convolutional neural network and active learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 58, 4604-4616(2020).

[19] Rehman A, Naz S, Razzak M I et al. Automatic visual features for writer identification: a deep learning approach[J]. IEEE Access, 7, 17149-17157(2019).

[20] Wan Y, Yang H Y, Wang Y L et al. Recognition of rice disease based on image segmentation and convolution neural network[J]. Acta Agriculturae Boreali-Occidentalis Sinica, 31, 246-256(2022).

[21] Xu H Q, Chen B, Wang J F et al. Elongated pavement distress detection method based on convolutional neural network[J]. Journal of Computer Applications, 42, 265-272(2022).

[22] Ghazi M M, Yanikoglu B, Aptoula E. Plant identification using deep neural networks via optimization of transfer learning parameters[J]. Neurocomputing, 235, 228-235(2017).

[23] Zhang Y, Jin P J, Wang S et al. Studies on the mechanisms of the surface weathering disease and erosion of the bricks of the Xi'an city wall[J]. Relics and Museolgy, 106-112(2019).