{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T16:19:06Z","timestamp":1781713146476,"version":"3.54.5"},"reference-count":31,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2020,7,16]],"date-time":"2020-07-16T00:00:00Z","timestamp":1594857600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shanghai Sailing Program","award":["19YF1437200, 18YF1418600"],"award-info":[{"award-number":["19YF1437200, 18YF1418600"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Modern satellite and aerial imagery outcomes exhibit increasingly complex types of ground objects with continuous developments and changes in land resources. Single remote-sensing modality is not sufficient for the accurate and satisfactory extraction and classification of ground objects. Hyperspectral imaging has been widely used in the classification of ground objects because of its high resolution, multiple bands, and abundant spatial and spectral information. Moreover, the airborne light detection and ranging (LiDAR) point-cloud data contains unique high-precision three-dimensional (3D) spatial information, which can enrich ground object classifiers with height features that hyperspectral images do not have. Therefore, the fusion of hyperspectral image data with airborne LiDAR point-cloud data is an effective approach for ground object classification. In this paper, the effectiveness of such a fusion scheme is investigated and confirmed on an observation area in the middle parts of the Heihe River in China. By combining the characteristics of hyperspectral compact airborne spectrographic imager (CASI) data and airborne LiDAR data, we extracted a variety of features for data fusion and ground object classification. Firstly, we used the minimum noise fraction transform to reduce the dimensionality of hyperspectral CASI images. Then, spatio-spectral and textural features of these images were extracted based on the normalized vegetation index and the gray-level co-occurrence matrices. Further, canopy height features were extracted from airborne LiDAR data. Finally, a hierarchical fusion scheme was applied to the hyperspectral CASI and airborne LiDAR features, and the fused features were used to train a residual network for high-accuracy ground object classification. The experimental results showed that the overall classification accuracy was based on the proposed hierarchical-fusion multiscale dilated residual network (M-DRN), which reached an accuracy of 97.89%. This result was found to be 10.13% and 5.68% higher than those of the convolutional neural network (CNN) and the dilated residual network (DRN), respectively. Spatio-spectral and textural features of hyperspectral CASI images can complement the canopy height features of airborne LiDAR data. These complementary features can provide richer and more accurate information than individual features for ground object classification and can thus outperform features based on a single remote-sensing modality.<\/jats:p>","DOI":"10.3390\/s20143961","type":"journal-article","created":{"date-parts":[[2020,7,16]],"date-time":"2020-07-16T10:54:46Z","timestamp":1594896886000},"page":"3961","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Fusion of Hyperspectral CASI and Airborne LiDAR Data for Ground Object Classification through Residual Network"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2259-2276","authenticated-orcid":false,"given":"Zhanyuan","family":"Chang","sequence":"first","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China"},{"name":"College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huiling","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yizhuo","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keqi","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,16]]},"reference":[{"key":"ref_1","first-page":"25","article-title":"LiDAR remote sensing of the cryosphere: Present applications and future prospects","volume":"1771","author":"Bhardwaj","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"6687","DOI":"10.1109\/TGRS.2016.2587798","article-title":"Rapid Updating and Improvement of Airborne LIDAR DEMs Through Ground-Based SfM 3-D Modeling of Volcanic Features","volume":"54","author":"Kolzenburg","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Sothe, C., Dalponte, M., Almeida, C.M.D., Schimalski, M.B., Lima, C.L., Liesenberg, V., and Tommaselli, A.M.G. (2019). Tree Species Classification in a Highly Diverse Subtropical Forest Integrating UAV-Based Photogrammetric Point Cloud and Hyperspectral Data. Remote Sens., 11.","DOI":"10.3390\/rs11111338"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Abeysinghe, T., Simic Milas, A., Arend, K., Hohman, B., Reil, P., Gregory, A., and V\u00e1zquez-Ortega, A. (2019). Mapping Invasive Phragmites Australis in the Old Woman Creek Estuary Using UAV Remote Sensing and Machine Learning Classifiers. Remote Sens., 11.","DOI":"10.3390\/rs11111380"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Cao, J., Leng, W., Liu, K., Liu, L., He, Z., and Zhu, Y. (2018). Object-Based Mangrove Species Classification Using Unmanned Aerial Vehicle Hyperspectral Images and Digital Surface Models. Remote Sens., 10.","DOI":"10.3390\/rs10010089"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4136","DOI":"10.1080\/01431161.2020.1714771","article-title":"UAV-hyperspectral imaging of spectrally complex environments","volume":"41","author":"Banerjee","year":"2020","journal-title":"Int. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4153","DOI":"10.1109\/JSTARS.2014.2312717","article-title":"A Two-Phase Classification of Urban Vegetation Using Airborne LiDAR Data and Aerial Photography","volume":"7","author":"Tong","year":"2014","journal-title":"IEEE Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.isprsjprs.2020.04.021","article-title":"Combining single photon and multispectral airborne laser scanning for land cover classification","volume":"1642","author":"Matikainen","year":"2020","journal-title":"ISPRS Photogramm. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"922","DOI":"10.3390\/rs70100922","article-title":"Object-Based Crop Species Classification Based on the Combination of Airborne Hyperspectral Images and LiDAR Data","volume":"7","author":"Liu","year":"2015","journal-title":"Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1080\/15481603.2016.1177249","article-title":"Integration of full-waveform Li DAR and hyperspectral data to enhance tea and areca classification","volume":"53","author":"Chu","year":"2016","journal-title":"GISci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/j.ecolind.2017.10.066","article-title":"Predicting stem diameters and aboveground biomass of individual trees using remote sensing data","volume":"85","author":"Dalponte","year":"2018","journal-title":"Ecol. Indic."},{"key":"ref_12","first-page":"67","article-title":"Inverse Coefficient of Variation Feature and Multilevel Fusion Technique for Hyperspectral and LiDAR Data Classification","volume":"133","author":"Jahan","year":"2020","journal-title":"IEEE Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1925","DOI":"10.1002\/hyp.8217","article-title":"Characterising soil moisture in transport corridor environments using airborne LIDAR and CASI data","volume":"26","author":"Hardy","year":"2012","journal-title":"Hydrol. Process."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhang, L., Liu, Z., Ren, T., Liu, D., Ma, Z., Tong, L., Zhang, C., Zhou, T., Zhang, X., and Li, S. (2020). Identification of Seed Maize Fields with High Spatial Resolution and Multiple Spectral Remote Sensing Using Random Forest Classifier. Remote Sens., 12.","DOI":"10.3390\/rs12030362"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1080\/01431161.2014.995276","article-title":"Integrating multiple texture methods and NDVI to the Random Forest classification algorithm to detect tea and hazelnut plantation areas in northeast Turkey","volume":"36","author":"Akar","year":"2015","journal-title":"Int. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3742","DOI":"10.1109\/TGRS.2013.2275613","article-title":"Feature Extraction of Hyperspectral Images with Image Fusion and Recursive Filtering","volume":"52","author":"Kang","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2025","DOI":"10.1016\/j.rse.2011.04.004","article-title":"Optimising the use of hyperspectral and LiDAR data for mapping reedbed habitats","volume":"115","author":"Onojeghuo","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Deng, W., Liu, H., Xu, J., Zhao, H., and Song, Y. (2020). An Improved Quantum-Inspired Differential Evolution Algorithm for Deep Belief Network. IEEE Trans. Instrum. Meas.","DOI":"10.1109\/TIM.2020.2983233"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1109\/TGRS.2017.2748160","article-title":"Unsupervised Spectral-Spatial Feature Learning via Deep Residual Conv-Deconv Network for Hyperspectral Image Classification","volume":"56","author":"Mou","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1970","DOI":"10.1109\/LGRS.2016.2619354","article-title":"Deep Learning With Attribute Profiles for Hyperspectral Image Classification","volume":"13","author":"Aptoula","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"147","DOI":"10.4314\/wsa.v34i2.183634","article-title":"A Comparison of Satellite Hyperspectral and Multispectral Remote Sensing Imagery for Improved Classification and Mapping of Vegetation","volume":"34","author":"Govender","year":"2008","journal-title":"Water SA"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"258619","DOI":"10.1155\/2015\/258619","article-title":"Deep Convolutional Neural Networks for Hyperspectral Image Classification","volume":"2015","author":"Hu","year":"2015","journal-title":"J. Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4544","DOI":"10.1109\/TGRS.2016.2543748","article-title":"Spectral\u2013Spatial Feature Extraction for Hyperspectral Image Classification: A Dimension Reduction and Deep Learning Approach","volume":"54","author":"Zhao","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1109\/TGRS.2017.2755542","article-title":"Spectral-Spatial Residual Network for Hyperspectral Image Classification: A 3-D Deep Learning Framework","volume":"56","author":"Zhong","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","first-page":"86","article-title":"Feature extraction scheme for a textural hyperspectral image classification using gray-scaled HSV and NDVI image features vectors fusion","volume":"1","author":"Ponomaryov","year":"2016","journal-title":"Int. Conf. Electron. Commun. Comput."},{"key":"ref_26","first-page":"03296","article-title":"Hyperspectral Image Classification Using CNN with Spectral and Spatial Features Integration","volume":"1071","author":"Vaddi","year":"2020","journal-title":"Infrared Phys. Technol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"676","DOI":"10.1109\/JPROC.2012.2229082","article-title":"Feature Mining for Hyperspectral Image Classification","volume":"101","author":"Ia","year":"2013","journal-title":"Proc. IEEE"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1109\/TIP.2010.2076296","article-title":"Kernel maximum autocorrelation factor and minimum noise fraction transformations","volume":"20","author":"Nielsen","year":"2010","journal-title":"IEEE Trans. Image Process."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3216","DOI":"10.1016\/j.patcog.2015.04.013","article-title":"Segmented minimum noise fraction transformation for efficient feature extraction of hyperspectral images","volume":"48","author":"Guan","year":"2015","journal-title":"Pattern Recognit."},{"key":"ref_30","first-page":"3501","article-title":"Hyperspectral image classification based on hierarchical fusion of residual networks","volume":"39","author":"Zhang","year":"2019","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.sysarc.2019.02.008","article-title":"Filter-based deep-compression with global average pooling for convolutional networks","volume":"95","author":"Hsiao","year":"2019","journal-title":"Syst. Archit."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/14\/3961\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:49:04Z","timestamp":1760176144000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/14\/3961"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,16]]},"references-count":31,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2020,7]]}},"alternative-id":["s20143961"],"URL":"https:\/\/doi.org\/10.3390\/s20143961","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,16]]}}}