Abstract
In the field of remote sensing imaging, multispectral imaging can obtain an image of the observed scene in several bands, while the light detection and ranging (LiDAR) can acquire the accurate 3D geometric information of the scene. With the development of remote sensing technology, how to effectively integrate the two imaging technologies in order to collect and process simultaneous spectral and 3D geometric information has been one of the frontier problems. Most of the present researches on simultaneous spectral and geometric data acquisition focus on the design of physical multispectral LiDAR system, which inevitably lead to an imaging system of heavy weight and high power consumption and thus inconvenient in practice. Different from the present researches, a UAV-based integrated multispectral-LiDAR system is introduced in this paper. Through simultaneous multi-sensor data collection and multispectral point cloud generation, a low-cost and UAV-based portable 3D geometric and spectral information acquisition system can be achieved.
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References
Gu Y F, Wang Q W, Jia X P, et al. A novel MKL model of integrating LiDAR data and MSI for urban area classification. IEEE Trans Geosci Remote Sens, 2015, 53: 5312–5326
Gu Y F, Wang Q W. Discriminative graph-based fusion of HSI and LiDAR data for urban area classification. IEEE Geosci Remote Sens Lett, 2017, 14: 906–910
Hong D, Chanussot J, Yokoya N, et al. Learning-shared cross-modality representation using multispectral-LiDAR and hyperspectral data. IEEE Geosci Remote Sens Lett, 2020, 1–5
Hong D, Yokoya N, Chanussot J, et al. CoSpace: Common subspace learning from hyperspectral-multispectral correspondences. IEEE Trans Geosci Remote Sens, 2019, 57: 4349–4359
Hong D, Yokoya N, Ge N, et al. Learnable manifold alignment (LeMA): A semi-supervised cross-modality learning framework for land cover and land use classification. ISPRS J Photogramm Remote Sens, 2019, 147: 193–205
Woodhouse I H, Nichol C, Sinclair P, et al. A multispectral canopy LiDAR demonstrator project. IEEE Geosci Remote Sens Lett, 2011, 8: 839–843
Puttonen E, Jaakkola A, Litkey P, et al. Tree classification with fused mobile laser scanning and hyperspectral data. Sensors, 2011, 11: 5158–5182
Dinuls R, Erins G, Lorencs A, et al. Tree species identification in mixed Baltic forest using LiDAR and multispectral data. IEEE J Sel Top Appl Earth Observ Remote Sens, 2012, 5: 594–603
Huang X, Zhang L. An SVM ensemble approach combining spectral, structural, and semantic features for the classification of high-resolution remotely sensed imagery. IEEE Trans Geosci Remote Sens, 2013, 51: 257–272
Liao W Z, Pizurica A, Bellens R, et al. Generalized graph-based fusion of hyperspectral and LiDAR data using morphological features. IEEE Geosci Remote Sens Lett, 2015, 12: 552–556
Zhang Y, Prasad S. Multisource geospatial data fusion via local joint sparse representation. IEEE Trans Geosci Remote Sens, 2016, 54: 3265–3276
Wallace A M, McCarthy A, Nichol C J, et al. Design and evaluation of multispectral lidar for the recovery of arboreal parameters. IEEE Trans Geosci Remote Sens, 2013, 52: 4942–4954
Niu Z, Xu Z G, Sun G, et al. Design of a new multispectral waveform LiDAR instrument to monitor vegetation. IEEE Geosci Remote Sens Lett, 2015, 12: 1506–1510
Nevalainen O, Hakala T, Suomalainen J, et al. Nitrogen concentration estimation with hyperspectral LiDAR. ISPRS Ann Photogramm Remote Sens Spatial Inf Sci, 2013, II-5/W2: 205–210
Pedergnana M, Marpu P R, Dalla Mura M, et al. Classification of remote sensing optical and LiDAR data using extended attribute profiles. IEEE J Sel Top Signal Process, 2012, 6: 856–865
Hakala T, Suomalainen J, Kaasalainen S, et al. Full waveform hyperspectral LiDAR for terrestrial laser scanning. Opt Express, 2012, 20: 7119–7127
Fernandez-Diaz J, Carter W, Glennie C, et al. Capability assessment and performance metrics for the Titan multispectral mapping LiDAR. Remote Sens, 2016, 8: 936
Feigels V I, Kopilevich Y I. Lasers for lidar bathymetry and oceanographic research: Choice criteria. In: Proceedings of IGARSS’94-1994, IEEE International Geoscience and Remote Sensing Symposium. Pasadena, 1994. 475–478
Fernandez-Diaz J C, Glennie C L, Carter W E, et al. Early results of simultaneous terrain and shallow water bathymetry mapping using a single-wavelength airborne LiDAR sensor. IEEE J Sel Top Appl Earth Observ Remote Sens, 2013, 7: 623–635
Chen Y, Räikkönen E, Kaasalainen S, et al. Two-channel hyperspectral LiDAR with a supercontinuum laser source. Sensors, 2010, 10: 7057–7066
Morsy S, Shaker A, El-Rabbany A, et al. Airborne multispectral Li- DAR data for land-cover classification and land/water mapping using different spectral indexes. ISPRS Ann Photogramm Remote Sens Spatial Inf Sci, 2016, III-3: 217–224
Briese C, Pfennigbauer M, Ullrich A, et al. Multi-wavelength airborne laser scanning for archaeological prospection. Int Arch Photogramm Remote Sens Spatial Inf Sci, 2013, XL-5/W2: 119–124
Gong K, Fritsch D. Point cloud and digital surface model generation from high resolution multiple view stereo satellite imagery. Int Arch Photogramm Remote Sens Spatial Inf Sci, 2018, XLII-2: 363–370
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This work was supported by the National Natural Science Foundation of Key International Cooperation (Grant No. 61720106002), the Key Research and Development Project of Ministry of Science and Technology (Grant No. 2017YFC1405100), and the Heading Wild Goose Plan of Heilongjiang Province, China.
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Gu, Y., Jin, X., Xiang, R. et al. UAV-based integrated multispectral-LiDAR imaging system and data processing. Sci. China Technol. Sci. 63, 1293–1301 (2020). https://doi.org/10.1007/s11431-019-1571-0
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DOI: https://doi.org/10.1007/s11431-019-1571-0