Subject Areas : International Journal of Smart Electrical Engineering
1 - Faculty of Geodesy and Geomatics Engineering, K.N.Toosi University of Technology
Keywords:
Abstract :
References:
[1] Michelini, D., Dalponte, M., Carriero, A., Kutchartt, E., Pappalardo, S. E., De Marchi, M., & Pirotti, F, "Hyperspectral and LiDAR data for the prediction via machine learning of tree species, volume and biomass: A contribution for updating forest management plans" Italian Conference on Geomatics and Geospatial Technologies; 2022: Springer. doi: 10.1007/978-3-031-17439-1_17
[2] Liu Y, Ye Z, Xi Y, Liu H, Li W, Bai L, "Multi-Scale and Multi-Direction Feature Extraction Network for Hyperspectral and LiDAR Classification" IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2024, vol. 17, pp. 9961 - 9973, doi: 10.1109/JSTARS.2024.3400872.
[3] Zhang H, Yao J, Ni L, Gao L, Huang M, "Multimodal attention-aware convolutional neural networks for classification of hyperspectral and LiDAR data" IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2022, vol.16, pp. 3635-3644, doi: 10.1109/JSTARS.2022.3187730.
[4] Du X, Zheng X, Lu X, Wang X, "Hyperspectral and LiDAR Representation with Spectral-Spatial Graph Network" IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2023. vol. 16, pp. 9231 - 9245, doi: 10.1109/JSTARS.2023.3321776.
[5] Li J, Liu Y, Song R, Liu W, Li Y, Du Q, "HyperMLP: Superpixel Prior and Feature Aggregated Perceptron Networks for Hyperspectral and Lidar Hybrid Classification" IEEE Transactions on Geoscience and Remote Sensing. 2024, vol. 62, doi: 10.1109/TGRS.2024.3355037.
[6] Roy SK, Sukul A, Jamali A, Haut JM, Ghamisi P. "Cross hyperspectral and LiDAR attention transformer: An extended self-attention for land use and land cover classification" IEEE Transactions on Geoscience and Remote Sensing. 2024, vol. 62, doi: 10.1109/TGRS.2024.3374324.
[7] Hong D, Gao L, Hang R, Zhang B, Chanussot J, "Deep encoder–decoder networks for classification of hyperspectral and LiDAR data" IEEE Geoscience and Remote Sensing Letters. 2020, vol. 19, pp. 1-5, doi: 10.1109/LGRS.2020.3017414.
[8] Wang X, Feng Y, Song R, Mu Z, Song C, "Multi-attentive hierarchical dense fusion net for fusion classification of hyperspectral and LiDAR data" Information Fusion, 2022, vol. 82, pp. 1-18, doi: 10.1016/j.inffus.2021.12.008.
[9] Zhao G, Ye Q, Sun L, Wu Z, Pan C, Jeon B, "Joint classification of hyperspectral and LiDAR data using a hierarchical CNN and transformer" IEEE Transactions on Geoscience and Remote Sensing, 2022, vol.61, pp. 1-16, doi: 10.1109/TGRS.2022.3232498.
[10] Zhang M, Li W, Zhang Y, Tao R, Du Q, "Hyperspectral and LiDAR data classification based on structural optimization transmission" IEEE Transactions on Cybernetics. 2022, vol. 53, no. 5, pp. 3153 - 3164, doi: 10.1109/TCYB.2022.3169773.
[11] Anand R, Veni S, Geetha P, Subramoniam SR, "Extended morphological profiles analysis of airborne hyperspectral image classification using machine learning algorithms" International Journal of Intelligent Networks, 2021, vol. 2, pp. 1-6, doi: 10.1016/j.ijin.2020.12.006.
[12] Kumar B, Dikshit O, "Hyperspectral image classification based on morphological profiles and decision fusion" International Journal of Remote Sensing, 2017, vol.38, no.20, pp. 5830-54, doi: 10.1080/01431161.2017.1348636.
[13] Ghamisi P, Hoefle B, "LiDAR data classification using extinction profiles and a composite kernel support vector machine" IEEE Geoscience and Remote Sensing Letters, 2017, vol.14, no.5, pp. 659-63, doi: 10.1109/LGRS.2017.2669304.
[14] Wang A, He X, Ghamisi P, Chen Y, "LiDAR data classification using morphological profiles and convolutional neural networks" IEEE Geoscience and Remote Sensing Letters, 2018, vol.15, no.5, pp.774-8, doi: 10.1109/LGRS.2018.2810276.
[15] He X, Wang A, Ghamisi P, Li G, Chen Y, "LiDAR data classification using spatial transformation and CNN" IEEE Geoscience and Remote Sensing Letters, 2018, vol.16, no.1, pp.125-9, doi: 10.1109/LGRS.2018.2868378.
[16] Wang A, Wang M, Jiang K, Zhao L, Iwahori Y, "A novel lidar data classification algorithm combined densenet with STN" IEEE IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium;2019, doi: 10.1109/IGARSS.2019.8900313.
[17] Xie H, Chen Y, "LiDAR data classification based on automatic designed CNN" IEEE Geoscience and Remote Sensing Letters, 2020, vol.18, no.9, pp.1665-9, doi: 10.1109/LGRS.2020.3005209.
[18] Wang A, Wang M, Wu H, Jiang K, Iwahori Y. "A novel LiDAR data classification algorithm combined capsnet with resnet" Sensors, 2020, vol.20, no.4, pp.1151, doi: 10.3390/s20041151.
[19] Wu H, Cao M, Wang A, Wang M. "Classification of LiDAR data combined octave convolution with capsule network" IEEE Access, 2020, vol.8, pp.16155-65, doi: 10.1109/ACCESS.2020.2965278.
[20] Wang A, Xue D, Wu H, Iwahori Y, "LiDAR data classification based on improved conditional generative adversarial networks" IEEE Access. 2020 vol.8, pp.209674-86, doi: 10.1109/ACCESS.2020.3039211.
[21] Hariyono M, Tambunan M, Dewi R, "Support vector machine for land cover classification using lidar data" IOP Conference Series: Earth and Environmental Science; 2021, doi: 10.1088/1755-1315/873/1/012095.
[22] Asghari Beirami B, Mokhtarzade M, "Land Covers Classification from LiDAR-DSM Data Based on Local Kernel Matrix Features of Morphological Profiles" International Journal of Engineering, 2023, vol.36, no. 9, pp.1611-7, doi: 10.5829/IJE.2023.36.09C.04.
[23] Dong J, Liu K, Han J, Zhang M, Zhao X, Li W, X Li, M Rao, "Multi-scale Neighborhood Information Fusion Network for Classification of Remote Sensing LiDAR Images" IEEE Sensors Journal, 2024, vol.24, no.10, pp. 16601 - 16613, doi: 10.1109/JSEN.2024.3386173.
[24] Beirami BA, "Face Recognition based on Multi-shape Morphological Profiles-based Covariance Descriptors and Log-Etuclidean Kernel SVM" IEEE 9th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS); 2022, doi: 10.1109/CFIS54774.2022.9756491.
[25] Sharifi O, Mokhtarzadeh M, Asghari Beirami B, "A new deep learning approach for classification of hyperspectral images: Feature and decision level fusion of spectral and spatial features in multiscale CNN" Geocarto International, 2022, vol.37, no.14, pp.4208-33, doi: 10.1080/10106049.2021.1882006.
[26] Beirami BA, Mokhtarzade M, "A new deep learning approach for hyperspectral image classification based on multifeature local kernel descriptors" Advances in Space Research, 2023, vol.72, no.5, pp.1703-20, doi: 10.1016/j.asr.2023.04.025.
[27] Beirami BA, Mokhtarzade M, "SVM classification of hyperspectral images using the combination of spectral bands and Moran's I features" 2017 IEEE 10th Iranian Conference on Machine Vision and Image Processing (MVIP), 2017, doi: 10.1109/IranianMVIP.2017.8342334.
[1] Michelini, D., Dalponte, M., Carriero, A., Kutchartt, E., Pappalardo, S. E., De Marchi, M., & Pirotti, F, "Hyperspectral and LiDAR data for the prediction via machine learning of tree species, volume and biomass: A contribution for updating forest management plans" Italian Conference on Geomatics and Geospatial Technologies; 2022: Springer. doi: 10.1007/978-3-031-17439-1_17
[2] Liu Y, Ye Z, Xi Y, Liu H, Li W, Bai L, "Multi-Scale and Multi-Direction Feature Extraction Network for Hyperspectral and LiDAR Classification" IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2024, vol. 17, pp. 9961 - 9973, doi: 10.1109/JSTARS.2024.3400872.
[3] Zhang H, Yao J, Ni L, Gao L, Huang M, "Multimodal attention-aware convolutional neural networks for classification of hyperspectral and LiDAR data" IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2022, vol.16, pp. 3635-3644, doi: 10.1109/JSTARS.2022.3187730.
[4] Du X, Zheng X, Lu X, Wang X, "Hyperspectral and LiDAR Representation with Spectral-Spatial Graph Network" IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2023. vol. 16, pp. 9231 - 9245, doi: 10.1109/JSTARS.2023.3321776.
[5] Li J, Liu Y, Song R, Liu W, Li Y, Du Q, "HyperMLP: Superpixel Prior and Feature Aggregated Perceptron Networks for Hyperspectral and Lidar Hybrid Classification" IEEE Transactions on Geoscience and Remote Sensing. 2024, vol. 62, doi: 10.1109/TGRS.2024.3355037.
[6] Roy SK, Sukul A, Jamali A, Haut JM, Ghamisi P. "Cross hyperspectral and LiDAR attention transformer: An extended self-attention for land use and land cover classification" IEEE Transactions on Geoscience and Remote Sensing. 2024, vol. 62, doi: 10.1109/TGRS.2024.3374324.
[7] Hong D, Gao L, Hang R, Zhang B, Chanussot J, "Deep encoder–decoder networks for classification of hyperspectral and LiDAR data" IEEE Geoscience and Remote Sensing Letters. 2020, vol. 19, pp. 1-5, doi: 10.1109/LGRS.2020.3017414.
[8] Wang X, Feng Y, Song R, Mu Z, Song C, "Multi-attentive hierarchical dense fusion net for fusion classification of hyperspectral and LiDAR data" Information Fusion, 2022, vol. 82, pp. 1-18, doi: 10.1016/j.inffus.2021.12.008.
[9] Zhao G, Ye Q, Sun L, Wu Z, Pan C, Jeon B, "Joint classification of hyperspectral and LiDAR data using a hierarchical CNN and transformer" IEEE Transactions on Geoscience and Remote Sensing, 2022, vol.61, pp. 1-16, doi: 10.1109/TGRS.2022.3232498.
[10] Zhang M, Li W, Zhang Y, Tao R, Du Q, "Hyperspectral and LiDAR data classification based on structural optimization transmission" IEEE Transactions on Cybernetics. 2022, vol. 53, no. 5, pp. 3153 - 3164, doi: 10.1109/TCYB.2022.3169773.
[11] Anand R, Veni S, Geetha P, Subramoniam SR, "Extended morphological profiles analysis of airborne hyperspectral image classification using machine learning algorithms" International Journal of Intelligent Networks, 2021, vol. 2, pp. 1-6, doi: 10.1016/j.ijin.2020.12.006.
[12] Kumar B, Dikshit O, "Hyperspectral image classification based on morphological profiles and decision fusion" International Journal of Remote Sensing, 2017, vol.38, no.20, pp. 5830-54, doi: 10.1080/01431161.2017.1348636.
[13] Ghamisi P, Hoefle B, "LiDAR data classification using extinction profiles and a composite kernel support vector machine" IEEE Geoscience and Remote Sensing Letters, 2017, vol.14, no.5, pp. 659-63, doi: 10.1109/LGRS.2017.2669304.
[14] Wang A, He X, Ghamisi P, Chen Y, "LiDAR data classification using morphological profiles and convolutional neural networks" IEEE Geoscience and Remote Sensing Letters, 2018, vol.15, no.5, pp.774-8, doi: 10.1109/LGRS.2018.2810276.
[15] He X, Wang A, Ghamisi P, Li G, Chen Y, "LiDAR data classification using spatial transformation and CNN" IEEE Geoscience and Remote Sensing Letters, 2018, vol.16, no.1, pp.125-9, doi: 10.1109/LGRS.2018.2868378.
[16] Wang A, Wang M, Jiang K, Zhao L, Iwahori Y, "A novel lidar data classification algorithm combined densenet with STN" IEEE IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium;2019, doi: 10.1109/IGARSS.2019.8900313.
[17] Xie H, Chen Y, "LiDAR data classification based on automatic designed CNN" IEEE Geoscience and Remote Sensing Letters, 2020, vol.18, no.9, pp.1665-9, doi: 10.1109/LGRS.2020.3005209.
[18] Wang A, Wang M, Wu H, Jiang K, Iwahori Y. "A novel LiDAR data classification algorithm combined capsnet with resnet" Sensors, 2020, vol.20, no.4, pp.1151, doi: 10.3390/s20041151.
[19] Wu H, Cao M, Wang A, Wang M. "Classification of LiDAR data combined octave convolution with capsule network" IEEE Access, 2020, vol.8, pp.16155-65, doi: 10.1109/ACCESS.2020.2965278.
[20] Wang A, Xue D, Wu H, Iwahori Y, "LiDAR data classification based on improved conditional generative adversarial networks" IEEE Access. 2020 vol.8, pp.209674-86, doi: 10.1109/ACCESS.2020.3039211.
[21] Hariyono M, Tambunan M, Dewi R, "Support vector machine for land cover classification using lidar data" IOP Conference Series: Earth and Environmental Science; 2021, doi: 10.1088/1755-1315/873/1/012095.
[22] Asghari Beirami B, Mokhtarzade M, "Land Covers Classification from LiDAR-DSM Data Based on Local Kernel Matrix Features of Morphological Profiles" International Journal of Engineering, 2023, vol.36, no. 9, pp.1611-7, doi: 10.5829/IJE.2023.36.09C.04.
[23] Dong J, Liu K, Han J, Zhang M, Zhao X, Li W, X Li, M Rao, "Multi-scale Neighborhood Information Fusion Network for Classification of Remote Sensing LiDAR Images" IEEE Sensors Journal, 2024, vol.24, no.10, pp. 16601 - 16613, doi: 10.1109/JSEN.2024.3386173.
[24] Beirami BA, "Face Recognition based on Multi-shape Morphological Profiles-based Covariance Descriptors and Log-Etuclidean Kernel SVM" IEEE 9th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS); 2022, doi: 10.1109/CFIS54774.2022.9756491.
[25] Sharifi O, Mokhtarzadeh M, Asghari Beirami B, "A new deep learning approach for classification of hyperspectral images: Feature and decision level fusion of spectral and spatial features in multiscale CNN" Geocarto International, 2022, vol.37, no.14, pp.4208-33, doi: 10.1080/10106049.2021.1882006.
[26] Beirami BA, Mokhtarzade M, "A new deep learning approach for hyperspectral image classification based on multifeature local kernel descriptors" Advances in Space Research, 2023, vol.72, no.5, pp.1703-20, doi: 10.1016/j.asr.2023.04.025.
[27] Beirami BA, Mokhtarzade M, "SVM classification of hyperspectral images using the combination of spectral bands and Moran's I features" 2017 IEEE 10th Iranian Conference on Machine Vision and Image Processing (MVIP), 2017, doi: 10.1109/IranianMVIP.2017.8342334.
