2024:
Yan, K.*, Wang, J., Peng, R., Yang, K.,Chen, X., Yin, G., Dong, J., Weiss, M., Pu, J., Myneni, R.B., 2024. HiQ-LAI: ahigh-quality reprocessed MODIS leaf area index dataset with betterspatiotemporal consistency from 2000 to 2022. Earth Syst. Sci. Data 16,1601–1622. http://doi.org/10.5194/essd-16-1601-2024
Pu, J., Yan, K.*, Roy, S., Zhu, Z., Rautiainen, M., Knyazikhin, Y., Myneni,R.B., 2024. Sensor-independent LAI/FPAR CDR: reconstructing a globalsensor-independent climate data record of MODIS and VIIRS LAI/FPAR from 2000 to2022. Earth Syst. Sci. Data 16, 15–34. http://doi.org/10.5194/essd-16-15-2024
Yang, K., Yan, K.*, Zhang, X., Zhong, R., Chi, H., Liu, J., Ma, X., Wang, Y.,2024. Assessing FY-3D MERSI-II Observations for Vegetation Dynamics Monitoring:A Performance Test of Land Surface Reflectance. IEEE Trans. Geosci. RemoteSensing 62, 1–20. http://doi.org/10.1109/TGRS.2023.3348997
Yan, K., Zhang, X., Peng, R., Gao, S.,Liu, J., 2024. The Impact of Quality Control Methods on Vegetation MonitoringUsing MODIS FPAR Time Series. Forests 15, 553. http://doi.org/10.3390/f15030553
Zhong, R., Yan, K.*, Gao, S., Yang, K., Zhao, S., Ma, X., Zhu, P., Fan, L.,Yin, G., 2024. Response of grassland growing season length to extreme climaticevents on the Qinghai-Tibetan Plateau. Science of The Total Environment 909,168488. http://doi.org/10.1016/j.scitotenv.2023.168488
Zhu,X., Ma, X., Zhang, Z., Liu, Y., Luo, Y., Yan,K., Pei, T., Huete, A., 2024. Floating in the air:forecasting allergenic pollen concentration for managing urban public health.International Journal of Digital Earth 17, 2306894. http://doi.org/10.1080/17538947.2024.2306894
2023:
Gao, Si, Zhong, R., Yan, K.*, Ma, X., Chen, X., Pu, J.,Gao, Sicong, Qi, J., Yin, G., Myneni, R.B., 2023. Evaluating the saturationeffect of vegetation indices in forests using 3D radiative transfer simulationsand satellite observations. Remote Sensing of Environment 295, 113665. http://doi.org/10.1016/j.rse.2023.113665
Pu, J., Yan, K.*, Gao, S., Zhang, Y., Park, T., Sun, X., Weiss, M.,Knyazikhin, Y., Myneni, R.B., 2023. Improving the MODIS LAI compositing usingprior time-series information. Remote Sensing of Environment 287, 113493. http://doi.org/10.1016/j.rse.2023.113493
Wang, J., Yan, K.*, Gao, S., Pu, J., Liu, J., Park, T., Bi, J., Maeda, E.E.,Heiskanen, J., Knyazikhin, Y., Myneni, R.B., 2023. Improving the Quality ofMODIS LAI Products by Exploiting Spatiotemporal Correlation Information. IEEETrans. Geosci. Remote Sensing 61, 1–19. http://doi.org/10.1109/TGRS.2023.3264280
Sun,G., Pan, Z., Zhang, A., Jia, X., Ren, J., Fu, H., Yan, K., 2023. Large Kernel Spectral andSpatial Attention Networks for Hyperspectral Image Classification. IEEE Trans.Geosci. Remote Sensing 61, 1–15. http://doi.org/10.1109/TGRS.2023.3292065
Xu, T., Yan, K.*, He, Y., Gao, S., Yang, K., Wang, J., Liu, J., Liu, Z.,2023. Spatio-Temporal Variability Analysis of Vegetation Dynamics in China from2000 to 2022 Based on Leaf Area Index: A Multi-Temporal Image ClassificationPerspective. Remote Sensing15, 2975. http://doi.org/10.3390/rs15122975
Sun,G., Li, Z., Zhang, A., Wang, X., Yan, K.,Jia, X., Liu, Q., Li, J., 2023. A 10-m resolutionimpervious surface area map for the greater Mekong subregion from remotesensing images. Sci Data 10, 607. http://doi.org/10.1038/s41597-023-02518-z
Li, H., Yan, K.*, Gao, S., Ma, X., Zeng, Y., Li, W., Yin, G., Mu, X., Yan,G., Myneni, R.B., 2023. A Novel Inversion Approach for the Kernel-Driven BRDFModel for Heterogeneous Pixels. J Remote Sens 3, 0038. http://doi.org/10.34133/remotesensing.0038
Chen, R., Yin, G., Zhao, W., Yan, K., Wu, S., Hao, D., Liu, G.,2023. Topographic Correction of Optical Remote Sensing Images in MountainousAreas: A systematic review. IEEE Geosci. Remote Sens. Mag. 2–22. http://doi.org/10.1109/MGRS.2023.3311100
Gao, Y., Yang,T., Ye, Z., Lin, J., Yan, K., Bi,J., 2023. Global vegetation greenness interannual variability and itsevolvement in recent decades. Environ. Res. Commun. 5, 051011. http://doi.org/10.1088/2515-7620/acd74d
Lin, Y., Liu,S., Yan, L., Yan, K., Zeng, Y.,Yang, B., 2023. Improving the estimation of canopy structure using spectralinvariants: Theoretical basis and validation. Remote Sensing of Environment284, 113368. http://doi.org/10.1016/j.rse.2022.113368
Liu, X., Chen,Y., Mu, X., Yan, G., Xie, D., Ma, X., Yan,K., Song, W., Liu, Z., 2023. Correction for the Sun-Angle Effect on theNDVI Based on Path Length. IEEE Trans. Geosci. Remote Sensing 61, 1–17. http://doi.org/10.1109/TGRS.2023.3322780
Pan, Y., Peng,D., Chen, J.M., Myneni, R.B., Zhang, X., Huete, A.R., Fu, Y.H., Zheng, S., Yan, K., Yu, L., Zhu, P., Shen, M., Ju,W., Zhu, W., Xie, Q., Huang, W., Chen, Z., Huang, J., Wu, C., 2023.Climate-driven land surface phenology advance is overestimated due to ignoringland cover changes. Environ. Res. Lett. 18, 044045. http://doi.org/10.1088/1748-9326/acca34
2022:
Yan, K.*, Gao, S., Chi, H., Qi, J.,Song, W., Tong, Y., Mu, X., Yan, G., 2022a. Evaluation of theVegetation-Index-Based Dimidiate Pixel Model for Fractional Vegetation CoverEstimation. IEEE Trans. Geosci. Remote Sensing 60, 1–14. http://doi.org/10.1109/TGRS.2020.3048493
Yan, K., Li, H., Song, W., Tong, Y., Hao, D.,Zeng, Y., Mu, X., Yan, G., Fang, Y., Myneni, R.B., Schaaf, C., 2022b. Extending a Linear Kernel-Driven BRDF Model to RealisticallySimulate Reflectance Anisotropy Over Rugged Terrain. IEEE Trans. Geosci. RemoteSensing 60, 1–16. http://doi.org/10.1109/TGRS.2021.3064018
Chi,H., Yan, K.*, Yang, K., Du, S., Li,H., Qi, J., Zhou, W., 2022. Evaluation of TopographicCorrection Models Based on 3-D Radiative Transfer Simulation. IEEE Geosci.Remote Sensing Lett. 19, 1–5. http://doi.org/10.1109/LGRS.2021.3110907
Li, H., Yan, K.*, Gao, S., Song, W., Mu, X.,2022. Revisiting the Performance of the Kernel-Driven BRDF Model Using FilteredHigh-Quality POLDER Observations. Forests 13, 435. http://doi.org/10.3390/f13030435
Zou, D., Yan, K.*, Pu, J., Gao, S., Li, W., Mu,X., Knyazikhin, Y., Myneni, R.B., 2022. Revisit the Performance of MODIS andVIIRS Leaf Area Index Products from the Perspective of Time-Series Stability.IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing 15, 8958–8973. http://doi.org/10.1109/JSTARS.2022.3214224
Liu, Y., Zhou, W., Gao, S., Ma,X., Yan, K., 2022a. PhenologicalResponses to Snow Seasonality in the Qilian Mountains Is a Function of BothElevation and Vegetation Types. Remote Sensing 14, 3629. http://doi.org/10.3390/rs14153629
Liu, Y., Zhou,W., Yan, K., Guan, Y., Wang, J.,2022b. Identification of the disturbed range of coal mining activities: A newland surface phenology perspective. Ecological Indicators 143, 109375. http://doi.org/10.1016/j.ecolind.2022.109375
Zhao, Y., Wang, M., Zhao, T.,Luo, Y., Li, Y., Yan, K., Lu, L.,Tran, N.N., Wu, X., Ma, X., 2022. Evaluating the potential of H8/AHIgeostationary observations for monitoring vegetation phenology over differentecosystem types in northern China. International Journal of Applied EarthObservation and Geoinformation 112, 102933. http://doi.org/10.1016/j.jag.2022.102933
2021:
Yan, K.*, Pu, J., Park, T., Xu, B.,Zeng, Y., Yan, G., Weiss, M., Knyazikhin, Y., Myneni, R.B., 2021a. Performancestability of the MODIS and VIIRS LAI algorithms inferred from analysis of longtime series of products. Remote Sensing of Environment 260, 112438. http://doi.org/10.1016/j.rse.2021.112438
Yan, K.*, Zhang, Y., Tong,Y., Zeng, Y., Pu, J., Gao, S., Li, L., Mu, X., Yan, G., Rautiainen, M.,Knyazikhin, Y., Myneni, R.B., 2021b. Modeling the radiation regime of adiscontinuous canopy based on the stochastic radiative transport theory:Modification, evaluation and validation. Remote Sensing of Environment 267,112728. http://doi.org/10.1016/j.rse.2021.112728
Wang, J., Wang, S., Zou, D.,Chen, H., Zhong, R., Li, H., Zhou, W., Yan,K., 2021. Social Network and Bibliometric Analysis of Unmanned AerialVehicle Remote Sensing Applications from 2010 to 2021. Remote Sensing 13, 2912. http://doi.org/10.3390/rs13152912
Yan, K.*, Zou, D., Yan, G., Fang, H., Weiss, M.,Rautiainen, M., Knyazikhin, Y., Myneni, R.B., 2021. ABibliometric Visualization Review of the MODIS LAI/FPAR Products from 1995 to2020. J Remote Sens 2021, 7410921. http://doi.org/10.34133/2021/7410921
付东杰, 肖寒, 苏奋振, 周成虎, 董金玮, 曾也鲁, 闫凯, 李世卫, 吴进, 吴文周, 2021. 澳门·威斯尼斯网站云计算平台发展及地球科学应用. 澳门·威斯尼斯网站学报 25, 11.
刘钊, 闫凯, 王铸, 蔡闻佳, 史培军, 2021.1961-2020年中国31个城市热浪强度时空特征分析. 自然灾害学报 30, 9.
谢涓, 闫凯, 康志忠, 徐箫剑, 薛彬, 杨建峰, 陶金有, 2021. “祝融号”火星车多光谱相机岩矿类型识别的地面验证研究. 澳门·威斯尼斯网站学报 25, 15.
闫凯*, 陈慧敏, 付东杰, 曾也鲁, 董金玮, 李世卫, 吴秋生, 李翰良, 杜姝渊, 2022. 澳门·威斯尼斯网站云计算平台相关文献计量可视化分析. 澳门·威斯尼斯网站学报 26, 14.
阎广建, 姜海兰, 闫凯, 程诗宇, 宋婉娟, 童依依, 刘雅楠, 漆建波, 穆西晗, 张吴明, 2021. 多角度光学定量澳门·威斯尼斯网站. 澳门·威斯尼斯网站学报 25, 26.
2020:
Pu, J., Yan, K.*, Zhou, G., Lei, Y., Zhu, Y., Guo, D., Li, H., Xu, L.,Knyazikhin, Y., Myneni, R.B., 2020. Evaluation of the MODIS LAI/FPAR AlgorithmBased on 3D-RTM Simulations: A Case Study of Grassland. Remote Sensing 12,3391. http://doi.org/10.3390/rs12203391
Cao, Y., Wang, Y., Peng, J.,Zhang, L., Xu, L., Yan, K., Li, L.,2020. DML-GANR: Deep Metric Learning With Generative Adversarial NetworkRegularization for High Spatial Resolution Remote Sensing Image Retrieval. IEEETrans. Geosci. Remote Sensing 58, 8888–8904. http://doi.org/10.1109/TGRS.2020.2991545
Li, X., Huang,H., Shabanov, N.V., Chen, L., Yan, K.,Shi, J., 2020. Extending the stochastic radiative transfer theory to simulateBRF over forests with heterogeneous distribution of damaged foliage inside oftree crowns. Remote Sensing of Environment 250, 112040. http://doi.org/10.1016/j.rse.2020.112040
Pu, J., Yan, K.*, Zhang, Y., Xu,L., 2020a. Quality Analysis of the VIIRS LAI/FPAR Time-Series,in: IGARSS 2020 - 2020 IEEE International Geoscience and Remote SensingSymposium. Presented at the IGARSS 2020 - 2020 IEEE International Geoscienceand Remote Sensing Symposium, IEEE, Waikoloa, HI, USA, pp. 3176–3179. http://doi.org/10.1109/IGARSS39084.2020.9323339
Xu, B., Li, J.,Park, T., Liu, Q., Zeng, Y., Yin, G., Yan,K., Chen, C., Zhao, J., Fan, W., Knyazikhin, Y., Myneni, R.B., 2020.Improving leaf area index retrieval over heterogeneous surface mixed withwater. Remote Sensing of Environment 240, 111700. http://doi.org/10.1016/j.rse.2020.111700
Yan, G., Chu,Q., Tong, Y., Mu, X., Qi, J., Zhou, Y., Liu, Y., Wang, T., Xie, D., Zhang, W., Yan, K., Chen, S., Zhou, H., 2020. AnOperational Method for Validating the Downward Shortwave Radiation Over RuggedTerrains. IEEE Trans. Geosci. Remote Sensing 1–18. http://doi.org/10.1109/TGRS.2020.2994384
Yin, G., Li, J., Xu, B., Zeng, Y., Wu, S., Yan, K., Verger, A., Liu, G., 2021. PLC-C:An Integrated Method for Sentinel-2 Topographic and Angular Normalization. IEEEGeosci. Remote Sensing Lett. 18, 1446–1450. http://doi.org/10.1109/LGRS.2020.3001905
Zeng, Y.,Badgley, G., Chen, M., Li, J., Anderegg, L.D.L., Kornfeld, A., Liu, Q., Xu, B.,Yang, B., Yan, K., Berry, J.A.,2020a. A radiative transfer model for solar induced fluorescence using spectralinvariants theory. Remote Sensing of Environment 240, 111678. http://doi.org/10.1016/j.rse.2020.111678
Zeng, Y., Li,J., Liu, Q., Huete, A.R., Xu, B., Yin, G., Fan, W., Ouyang, Y., Yan, K., Hao, D., Chen, M., 2020b. ARadiative Transfer Model for Patchy Landscapes Based on Stochastic RadiativeTransfer Theory. IEEE Trans. Geosci. Remote Sensing 58, 2571–2589. http://doi.org/10.1109/TGRS.2019.2952377
张寅、闫凯*、刘钊、濮嘉彬、张一满、曾也鲁, 2020. 基于CRU数据的1901—2018年全球陆表气温时空变化特征分析. 首都师范大学学报：自然科学版 41, 8.
2019:
Chu, Q., Yan, G., Wild, M., Zhou,Y., Yan, K., Li, L., Liu, Y., Tong,Y., Mu, X., 2019. Ground-Based Radiation Observational Method in MountainousAreas, in: IGARSS 2019 - 2019 IEEE International Geoscience and Remote SensingSymposium. Presented at the IGARSS 2019 - 2019 IEEE International Geoscienceand Remote Sensing Symposium, IEEE, Yokohama, Japan, pp. 8566–8569. http://doi.org/10.1109/IGARSS.2019.8900174
Yan, K.*, Tong, Y., Song, W., Zeng, Y., Liu, Z.,Mu, X., Yan, G., 2019. Analysis of the Kernel-DrivenBRDF Model Over Rugged Terrains, in: IGARSS 2019 - 2019 IEEE InternationalGeoscience and Remote Sensing Symposium. Presented at the IGARSS 2019 - 2019IEEE International Geoscience and Remote Sensing Symposium, IEEE, Yokohama,Japan, pp. 6807–6810. http://doi.org/10.1109/IGARSS.2019.8898377
2018:
Yan, G., Tong, Y., Yan, K.*, Mu, X., Chu, Q., Zhou, Y.,Liu, Y., Qi, J., Li, L., Zeng, Y., Zhou, H., Xie, D., Zhang, W., 2018. TemporalExtrapolation of Daily Downward Shortwave Radiation Over Cloud-Free RuggedTerrains. Part 1: Analysis of Topographic Effects. IEEE Trans. Geosci. RemoteSensing 56, 6375–6394. http://doi.org/10.1109/TGRS.2018.2838143
Yan, K., Park, T., Chen,C., Xu, B., Song, W., Yang, B., Zeng, Y., Liu, Z., Yan, G., Knyazikhin, Y.,Myneni, R.B., 2018. Generating Global Products of LAI and FPAR From SNPP-VIIRSData: Theoretical Background and Implementation. IEEE Trans. Geosci. RemoteSensing 56, 2119–2137. http://doi.org/10.1109/TGRS.2017.2775247
Chen,L., Mei, G., Yan, K., Hao, W., Yu,X., 2018. Species Discrimination of Plantations inSubtropical China Using 4-Band VHR Imagery and an Operational Image AnalysisFramework. IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing 11,2800–2813. http://doi.org/10.1109/JSTARS.2018.2837884
Li, L., Mu, X., Macfarlane, C., Song, W., Chen, J., Yan, K., Yan, G., 2018. A half-Gaussianfitting method for estimating fractional vegetation cover of corn crops usingunmanned aerial vehicle images. Agricultural and Forest Meteorology 262,379–390. http://doi.org/10.1016/j.agrformet.2018.07.028
Song, W.,Knyazikhin, Y., Wen, G., Marshak, A., Mõttus, M., Yan, K., Yang, B., Xu, B., Park, T., Chen, C., Zeng, Y., Yan, G.,Mu, X., Myneni, R.B., 2018. Implications of Whole-Disc DSCOVR EPIC SpectralObservations for Estimating Earth’s Spectral Reflectivity Based onLow-Earth-Orbiting and Geostationary Observations. Remote Sensing 10, 1594. http://doi.org/10.3390/rs10101594
Yin, G., Li, A., Wu, S., Fan, W., Zeng, Y., Yan, K., Xu, B., Li, J., Liu, Q., 2018. PLC:A simple and semi-physical topographic correction method for vegetationcanopies based on path length correction. Remote Sensing of Environment 215,184–198. http://doi.org/10.1016/j.rse.2018.06.009
Zeng, Y., Xu, B., Yin, G., Wu, S., Hu, G., Yan, K., Yang, B., Song, W., Li, J., 2018. SpectralInvariant Provides a Practical Modeling Approach for Future BiophysicalVariable Estimations. Remote Sensing 10, 1508. http://doi.org/10.3390/rs10101508
Zhou, Y., Yan,G., Zhao, J., Chu, Q., Liu, Y., Yan, K.,Tong, Y., Mu, X., Xie, D., Zhang, W., 2018. Estimation of Daily AverageDownward Shortwave Radiation over Antarctica. Remote Sensing 10, 422. http://doi.org/10.3390/rs10030422
2017:
Chen, C., Knyazikhin, Y., Park,T., Yan, K., Lyapustin, A., Wang,Y., Yang, B., Myneni, R., 2017. Prototyping of LAI and FPAR Retrievals from MODISMulti-Angle Implementation of Atmospheric Correction (MAIAC) Data. RemoteSensing 9, 370. http://doi.org/10.3390/rs9040370
Li, L., Yan, G.,Mu, X., Suhong, Liu, Chen, Y., Yan, K.,Luo, J., Song, W., 2017. Estimation of fractional vegetation cover usingmean-based spectral unmixing method, in: 2017 IEEE International Geoscience andRemote Sensing Symposium (IGARSS). Presented at the 2017 IEEE InternationalGeoscience and Remote Sensing Symposium (IGARSS), IEEE, Fort Worth, TX, pp.3178–3180. http://doi.org/10.1109/IGARSS.2017.8127672
Yang, B.,Knyazikhin, Y., Mõttus, M., Rautiainen, M., Stenberg, P., Yan, L., Chen, C., Yan, K., Choi, S., Park, T., Myneni,R.B., 2017. Estimation of leaf area index and its sunlit portion from DSCOVREPIC data: Theoretical basis. Remote Sensing of Environment 198, 69–84. http://doi.org/10.1016/j.rse.2017.05.033
2016:
Yan, K., Park, T., Yan, G., Chen, C.,Yang, B., Liu, Z., Nemani, R., Knyazikhin, Y., Myneni, R., 2016a. Evaluation ofMODIS LAI/FPAR Product Collection 6. Part 1: Consistency and Improvements.Remote Sensing 8, 359. http://doi.org/10.3390/rs8050359
Yan, K., Park, T., Yan,G., Liu, Z., Yang, B., Chen, C., Nemani, R., Knyazikhin, Y., Myneni, R., 2016b.Evaluation of MODIS LAI/FPAR Product Collection 6. Part 2: Validation andIntercomparison. Remote Sensing 8, 460. http://doi.org/10.3390/rs8060460
Bi, J., Myneni, R., Lyapustin,A., Wang, Y., Park, T., Chi, C., Yan, K.,Knyazikhin, Y., 2016. Amazon Forests’ Response to Droughts: A Perspective fromthe MAIAC Product. Remote Sensing 8, 356. http://doi.org/10.3390/rs8040356
Yang, B.,Knyazikhin, Y., Lin, Y., Yan, K.,Chen, C., Park, T., Choi, S., Mõttus, M., Rautiainen, M., Myneni, R., Yan, L.,2016. Analyses of Impact of Needle Surface Properties on Estimation of NeedleAbsorption Spectrum: Case Study with Coniferous Needle and Shoot Samples.Remote Sensing 8, 563. http://doi.org/10.3390/rs8070563
Zeng, Y., Li,J., Liu, Q., Huete, A.R., Xu, B., Yin, G., Zhao, J., Yang, L., Fan, W., Wu, S.,Yan, K., 2016a. An Iterative BRDF/NDVIInversion Algorithm Based on A Posteriori Variance Estimation of ObservationErrors. IEEE Trans. Geosci. Remote Sensing 54, 6481–6496. http://doi.org/10.1109/TGRS.2016.2585301
Zeng, Y., Li, J., Liu, Q., Huete, A.R., Yin, G., Xu, B., Fan, W., Zhao, J.,Yan, K., Mu, X., 2016b. A Radiative Transfer Model for Heterogeneous Agro-ForestryScenarios. IEEE Trans. Geosci. Remote Sensing 54, 4613–4628. http://doi.org/10.1109/TGRS.2016.2547326
Before2015:
Chen, Y., Zhang, W., Yan, K., Li, X., Zhou, G., 2012.Extracting corn geometric structural parameters using Kinect, in: 2012 IEEEInternational Geoscience and Remote Sensing Symposium. Presented at the IGARSS2012 - 2012 IEEE International Geoscience and Remote Sensing Symposium, IEEE,Munich, Germany, pp. 6673–6676. http://doi.org/10.1109/IGARSS.2012.6352068
Wang, H., Zhang,W., Chen, Y., Chen, M., Yan, K.,2015. Semantic Decomposition and Reconstruction of Compound Buildings withSymmetric Roofs from LiDAR Data and Aerial Imagery. Remote Sensing 7,13945–13974. http://doi.org/10.3390/rs71013945
Yan, G., Ren,H., Hu, R., Yan, K., Zhang, W.,2012. A portable Multi-Angle Observation System, in: 2012 IEEE InternationalGeoscience and Remote Sensing Symposium. Presented at the IGARSS 2012 - 2012IEEE International Geoscience and Remote Sensing Symposium, IEEE, Munich,Germany, pp. 6916–6919. http://doi.org/10.1109/IGARSS.2012.6352572
Yan, K.*, Ren, H., Hu, R., Mu, X., Liu, Z., Yan,G., 2013. Error analysis for emissivity measurementusing FTIR spectrometer, in: 2013 IEEE International Geoscience and RemoteSensing Symposium - IGARSS. Presented at the IGARSS 2013 - 2013 IEEEInternational Geoscience and Remote Sensing Symposium, IEEE, Melbourne,Australia, pp. 3080–3083. http://doi.org/10.1109/IGARSS.2013.6723477
Zhang, W., Wang,H., Chen, Y., Yan, K., Chen, M.,2014. 3D Building Roof Modeling by Optimizing Primitive’s Parameters UsingConstraints from LiDAR Data and Aerial Imagery. Remote Sensing 6, 8107–8133. http://doi.org/10.3390/rs6098107
