"1. LiDAR Remote Sensing and Applications" (January 2018)

 

 

"2. Introduction to LiDAR Remote Sensing" (June 2024)

 

Selected Journal Papers and Book Chapters:  

 

(Underlined authors -- graduate students)

 

[99] Duan, D., Deng, Y., Zhang, J., Wang, J., Dong, P.,  2024. Influence of VF and SOR filtering methods on tree height inversion using UAV-LiDAR data. Drones, (in press).

 

[98] Wu, J., Man, Q., Yang, X., Dong, P., Ma, X., Liu, C., Han, C.,  2024. Fine classification of urban tree species based on UAV-based RGB imagery and LiDAR data. Forests, 15(2), 390; https://doi.org/10.3390/f15020390.

 

[97] Li, B., He, R., Dong, P., Tian, J.,  2024. Comparison of convolutional neural network and support vector machine for identification of forest types and burned areas. Journal of Applied Remote Sensing. (in press)

 

[96] Luan, G., Zhao, F., Xia, J., Huang, Z., Feng, S., Song, C., Dong, P., Zhou, X.,  2024. Analysis of long-term spatio-temporal changes of plateau urban wetland reveals the response mechanisms of climate and human activities: A case study from Dianchi Lake Basin 1993–2020. Science of the Total Environment. https://doi.org/10.1016/j.scitotenv.2023.169447.

 

[95] Rahimi, E., Jahandideh, M., Dong, P., Ahmadzadeh, F.,  2023. Potential anthropogenic and climatic factors affecting Iran’s international wetlands. Journal of Environmental Studies and Sciences. https://doi.org/10.1007/s13412-023-00846-5.

 

[94] Huang, P., Zhao, X., Pu, J., Gu, Z., Feng, Y., Zhou, S., Shi, X., Tang, Y., Dong, P.,  2023. Linking random forest and auxiliary factors for extracting the major economic forests in the mountainous areas of southwestern Yunnan Province, China. Ecological Indicators Vol. 148. https://doi.org/10.1016/j.ecolind.2023.110025

 

[93] Tian, J., Dong, P., Xing, Y., Shan, W., Wang, Q., Li, D.,  2023. Comparison between biophysical analysis model and dimidiate pixel model for the estimation of forest canopy density. Journal of Applied Remote Sensing Vol. 17. https://doi.org/10.1117/1.JRS.17.014518.

 

[92] Ma, X., Man, Q., Yang, X., Dong, P., Yang, Z., Wu, J., Liu, C.,  2023. Urban feature extraction within a complex urban area with an improved 3D-CNN using airborne hyperspectral data. Remote Sensing 15(4). https://doi.org/10.3390/rs15040992.

 

[91] Rahimi, E., Dong, P.,  2023. Identifying barriers and pinch-points of large mammal corridors in Iran. Journal of Environmental Studies and Sciences 13:285–297.

 

[90] Isazade, V., Qasimi, A.B., Dong, P., Kaplan, G., Isazade, E.,  2023. Spatio-temporal modeling of COVID-19 outbreak in Qom and Mazandaran provinces, Iran. Modeling Earth Systems and Environment, https://doi.org/10.1007/s40808-023-01729-y

 

[89] Xia, J., Wang, Y., Dong, P., He, S., Zhao, F., Luan, G.,  2022. Object-oriented canopy gap extraction from UAV images based on edge enhancement. Remote Sensing. 14(19), 4762; https://doi.org/10.3390/rs14194762

 

[88] Mao, Z., Fan, L., Dong, P.,  2022. Modeling distance uncertainties in two-dimensional space. Measurement. https://doi.org/10.1016/j.measurement.2022.111818

 

[87] Rahimi, E., Dong, P.,  2022. Estimating the pollination supply of urban green spaces to determine suitable areas for urban agriculture in the City of Tehran. Urban Ecosystems. https://doi.org/10.1007/s11252-022-01289-6

 

[86] Rahimi, E., Dong, P.,  2022. What are the main human pressures affecting Iran's protected areas? Journal of Environmental Studies and Sciences 12: 682-691.

 

[85] Wang, Y., Dong, P., Liao, S., Zhu, Y., Zhang, D., and Yin, N.,  2022. Urban expansion monitoring based on the digital surface model—A case study of the Beijing–Tianjin–Hebei Plain. Applied Sciences, 2022, 12, 5312. https://doi.org/10.3390/app12115312

 

[84] Chang, Z., Dong, P., Yuan, R., Hou, J., Li, J., Chang, H.  2022. The 2014 northern Thailand Mw 6.1 earthquake and its seismogenic tectonics. Acta Geologica Sinica (English Edition), 2022, 96(2): 648–660.

 

[83] Rahimi, E., Barghjelveh, S., Dong, P.,  2022. A comparison of discrete and continuous metrics for measuring landscape changes. Journal of the Indian Society of Remote Sensing 50: 1257–1273.

 

[82] Rahimi, E., Barghjelveh, S., Dong, P.,  2022. Amount, distance-dependent and structural effects of forest patches on bees in agricultural landscapes: A review. Agriculture & Food Security 11(10), https://doi.org/10.1186/s40066-022-00360-x

 

[81] Zhang, J., Wang, J., Dong, P., Ma, W., Liu, Y., Liu, Q., Zhang, Z.,  2022. Tree stem extraction from point-cloud data of natural forests based on geometric features and DBSCAN. Geocarto International. https://doi.org/10.1080/10106049.2022.2034988

 

[80] Rahimi, E., Barghjelveh, S., Dong, P.,  2022. A review of diversity of bees, the attractiveness of host plants and the effects of landscape variables on bees in urban gardens. Agriculture & Food Security, 11(6), https://doi.org/10.1186/s40066-021-00353-2

 

[79] Rahimi, E., Barghjelveh, S., Dong, P., Pirlar, M.A., and Jahanbakhshian, M.M., 2021. PollMap: A software for crop pollination mapping in agricultural landscapes. Journal of Ecology and Environment, 45(27), https://doi.org/10.1186/s41610-021-00210-0

 

[78] Dong, P., Xia, J., Zhong, R., Zhao, Z., Tan, S., 2021. A new method for automated measurement of sand dune migration based on multi-temporal LiDAR-derived digital elevation models. Remote Sensing, 13, 3084. https:// doi.org/10.3390/rs13163084.

 

[77] Rahimi, E., Barghjelveh, S., Dong, P., 2021. Using the Lonsdorf and ESTIMAP models for large-scale pollination mapping (case study: Iran). Environmental Resources Research, 9: 235-251, DOI: 10.22069/IJERR.2021.18872.1332

 

[76] Rahimi, E., Barghjelveh, S., Dong, P., 2021. Quantifying how urban landscape heterogeneity affects land surface temperature at multiple scales. Journal of Ecology and Environment, 45(22), https://doi.org/10.1186/s41610-021-00203-z

 

[75] Rahimi, E., Barghjelveh, S., Dong, P., 2021. Estimating landscape structure effects on pollination for management of agricultural landscapes. Ecological Processes, 10(59), https://doi.org/10.1186/s13717-021-00331-3

 

[74] Rahimi, E., Barghjelveh, S., Dong, P., 2021. How effective are artificial nests in attracting bees? A review. Journal of Ecology and Environment, 45(16), https://doi.org/10.1186/s41610-021-00192-z

 

[73] Pu, J., Zhao, X., Dong, P., Wang, Q., Yue, Q., 2021. Extracting information on rocky desertification from satellite images: A comparative study. Remote Sensing 13(13), 2497.

 

[72] Rahimi, E., Barghjelveh, S., Dong, P., 2021. Estimating potential range shift of some wild bees in response to climate change scenarios in northwestern regions of Iran. Journal of Ecology and Environment, 45(14), https://doi.org/10.1186/s41610-021-00189-8

 

[71] Rahimi, E., Barghjelveh, S., Dong, P., 2021. Using the Lonsdorf model for estimating habitat loss and fragmentation effects on pollination service. Ecological Processes, 12. https://doi.org/10.1186/s13717-021-00291-8

 

[70] Liu, H., Dong, P., and Wu, C., Wang, P., Fang, M., 2021. Individual tree identification using a new cluster-based approach with discrete-return airborne LiDAR data. Remote Sensing of Environment 258: 112382.

 

[69] Wu, Q., Zhong, R., Dong, P., Mo, Y., Jin, Y., 2021. Airborne LiDAR intensity correction based on a new method for incidence angle correction for improving land-cover classification. Remote Sensing, 13(3), 511; https://doi.org/10.3390/rs13030511.

 

[68] Wang, Y., Dong, P., Zhu, Y., Shen, J., Liao, S., 2021. Geomorphic analysis of Xiadian buried fault zone in eastern Beijing plain based on SPOT image and unmanned aerial vehicle (UAV) data. Geomatics, Natural Hazards and Risk, Vol. 12, No. 1, pp. 261-278.

 

[67] Wang, R., Shi, W., and Dong, P., 2020.  Mapping dragon fruit croplands from space using remote sensing of artificial light at night. Remote Sensing, 12(24), 4139; https://doi.org/10.3390/rs12244139.

 

[66] Man, Q., Dong, P., Yang, X., Wu, Q., and Han, R., 2020. Automatic extraction of grasses and individual trees in urban area based on airborne hyperspectral and LiDAR data. Remote Sensing,12(17), 2725; https://doi.org/10.3390/rs12172725. 

 

[65] Hart, R., Liang, L., and Dong, P., 2020. Monitoring, mapping, and modeling spatial-temporal patterns of PM2.5 for improved understanding of air pollution dynamics using portable sensing technologies. International Journal of Environmental Research and Public Health. Vol. 17, No. 14, 4914; https://doi.org/10.3390/ijerph17144914.

 

[64] Liu, M., Dong, P., and Zhong, R., 2020. A rapid method for estimating the angle of repose and volume of grain piles using terrestrial laser scanning. Remote Sensing Letters, Vol. 11, No. 7, pp. 707-713.

 

[63] Dong, P., Zhong, R., Xia, J., and Tan, S., 2020. A semi-automated method for extracting channels and channel profiles from lidar-derived digital elevation models. Geosphere, Vol.16, No.3, pp. 806-816.

 

[62] Xia, J., Dong, P., and Zhao, Z., 2020. Selecting inter-city transportation routes in complex terrains using quantitative methods: A case study from Northern Yunnan, China. Promet - Traffic & Transportation. Vol. 32, No. 2, pp. 269-277.

 

[61] Dong, P., Sadeghinaeenifard, F., Xia, J., and Tan, S., 2019. Zonal lacunarity analysis: A new spatial analysis tool for geographic information systems. Landscape Ecology, Vol. 34, No. 10, pp. 2245-2249. https://doi.org/10.1007/s10980-019-00886-9

 

[60] Wang, Y., Dong, P., and Liao, S., 2019. Geomorphic analysis of the Beijing Plain based on ZY3 DEM and SRTM data. Quaternary Sciences, Vol. 39, No., 5, pp. 1211-1221.

 

[59] Xia, J., Li, J., Dong, P., Yang, K., 2019. An ArcGIS add-in for spatiotemporal data mining in climate data. Earth Science Informatics, DOI: 10.1007/s12145-019-00404-0.

 

[58] Peng, S., Xi, X., Wang, C., Dong, P., Wang, P., Nie, S.,2019. Systematic comparison of power corridor classification methods from ALS point clouds. Remote Sensing, 11, 1961; doi:10.3390/rs11171961.

 

[57] Wang, C., Qin, H., Zhao, K., Dong, P., Yang, X., Zhou, G., Xi, X., 2019. Assessing the impact of the built-up environment on nighttime lights in China. Remote Sensing, 11, 1712; doi:10.3390/rs11141712

 

[56] Man, Q. and Dong, P., 2019. Extraction of urban objects in cloud shadows based on fusion of airborne LiDAR and hyperspectral data. Remote Sensing, 11, 713; doi:10.3390/rs11060713.

 

[55] Sadeghinaeenifard, F. and Dong, P., 2019. Comparison of longitude-latitude and Euclidean distance shape descriptors for determining tree crown shapes derived from LiDAR data. International Journal of Remote Sensing, Vol. 40, No. 22.

 

[54] Xia, J. and Dong, P., 2019. Spatial characteristics of physical environments for human settlements in Jinsha River watershed (Yunnan Section), China. Geomatics, Natural Hazards and Risk, Vol. 10., pp. 544-561.

 

[53] Dong, P., Zhong, R., and Yigit, A., 2018. Automated parcel-based building change detection using multitemporal airborne LiDAR data. Surveying and Land Information Science, Vol. 77, No. 1, pp. 5-13.

 

[52] Liu, G., Wang, J., Dong, P., Chen, Y., and Liu, Z., 2018. Estimating individual tree height and diameter at breast height (DBH) from terrestrial laser scanning (TLS) data at plot level. Forests,9(398), 19 pages.

 

[51] Dong, P., 2017. Automated accuracy assessment for ridge and valley polylines using high-resolution digital elevation models. Geosphere, 13: 2078-2084.

 

[50] Nie, S., Wang, C., Dong, P., Li, G., and Xi, X., Luo, S., and Qin, H., 2017. A novel model for terrain slope estimation using ICESat/GLAS waveform data. IEEE Transactions on Geoscience and Remote Sensing, Vol. PP, No. 99, 1-11.

 

[49] Nie, S., Wang, C., Dong, P., Xi, X., Luo, S., and Qin, H., 2017. A revised progressive TIN densification for filtering airborne LiDAR data. Measurement, 104: 77-70.

 

[48] Xia, J., and Dong, P., 2016. A GIS add-in for automated measurement of sand dune migration using LiDAR-derived multi-temporal and high-resolution digital elevation models. Geosphere, Vol 12, No. 4, pp. 1316-1322.

 

[47] Nie, S., Wang, C., Dong, P., Xi, X., Luo, S., and Zhou, H., 2016. Estimating leaf area index of maize using airborne discrete-return LiDAR data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 9, No. 7, pp. 3259 - 3266.

 

[46] Li, D., Guo, H.D., Wang, C., Dong, P., and Zuo, Z., 2016. Improved bore-sight calibration for airborne LiDAR using planar patches. Journal of Applied Remote Sensing, 10(2), 024001 (2016), doi: 10.1117/1.JRS.10.024001.

 

[45] Dong, P., 2015. Automated measurement of sand dune migration using multi-temporal LiDAR data and GIS. International Journal of Remote Sensing, Vol. 36, No. 21, pp. 5526-5547.

 

[44] Nie, S., Wang, C., Dong, P., and Xi, X., 2015. Estimating leaf area index of maize using airborne full-waveform LiDAR data. Remote Sensing Letters, DOI: 10.1080/2150704X.2015.1111536.

 

[43] Xia, J., Dong, P., and Tang, J., 2015. Efficient rendering of natural hazards data in mobile GIS. Geomatics, Natural Hazards and Risk. DOI:10.1080/19475705.2015.1084954.

 

[42] Man, Q., Dong, P., and Guo, H.D., 2015. Pixel- and feature-level fusion of hyperspectral and LiDAR data for urban land use classification. International Journal of Remote Sensing, Vol. 36, No. 6, pp. 1618-1644.

 

[41] Dong, P., 2014. LiDAR data for characterizing linear and planar geomorphic markers in tectonic geomorphology. Journal of Geophysics and Remote Sensing 4: 136. doi:10.4172/2169-0049.1000136.

 

[40] Man, Q., Dong, P., and Guo, H.D., Liu, G., and Shi, R., 2014. LiDAR and hyperspectral data for estimation of forest biomass: A review. Journal of Applied Remote Sensing, 8(1), 081598 (2014). doi:10.1117/1.JRS.8.081598.

 

[39] Liu, H., and Dong, P., 2014. A new method for generating canopy height models from discrete-return LiDAR point clouds. Remote Sensing Letters. Vol. 5, No. 6, pp. 575-582.

 

[38] Liu, W., Dong, P., Liu, S., and Liu, J., 2014. A rich Internet application for automated detection of road blockage in post-disaster scenarios. IOP Conf. Series: Earth and Environmental Science 18 (2014) 012124. DOI:10.1088/1755-1315/18/1/012124. (Peer-reviewed).

 

[37] Zheng, S., Dong, P., Wang, C., Xi, X., and Lu, Y., 2014. Lacunarity analysis of LiDAR point clouds for tree crowns. Remote Sensing of Land Resources. Vol. 26, No. 4, pp. 103-110.

 

[36] Xiao, Y., Wang, C., Li, J., Zhang, W., Xi, X., Wang, C., and Dong, P., 2014. Building segmentation and modeling from airborne LiDAR data. International Journal of Digital Earth. DOI: 10.1080/17538947.2014.914252.

 

[35] Man, Q., Guo, H., Liu, G., and Dong, P., 2014. Comparison of different methods for monitoring glacier changes observed by Landsat images. IOP Conf. Series: Earth and Environmental Science 17 (2014) 012127. DOI:10.1088/1755-1315/17/1/012127. (Peer-reviewed).

 

[34] Dong, P., Wang, C., and Ding, J., 2013. Estimating glacier volume loss using remotely sensed images, digital elevation data, and GIS modeling. International Journal of Remote Sensing. Vol. 34, No. 24, pp. 8881-8892.

 

[33] Liu, W., Dong, P., Liu, J.B., and Guo, H.D., 2013. Evaluation of three-dimensional shape signatures for automated assessment of post-earthquake building damage. Earthquake Spectra. Vol. 29, No. 3, pp. 897-910.

 

[32] Tang, S., Dong, P., and Buckles, B., 2013. Three-dimensional surface reconstruction of tree canopy from LiDAR point clouds using a region-based level set method. International Journal of Remote Sensing. Vol. 34, No. 4, pp. 1373-1385.

 

[31] Dong, P., and Liu, J., 2012. Hyperspectral image classification using support vector machines with an efficient principal component analysis scheme. Advances in Intelligent and Soft Computing. Vol. 122, pp. 131-140.

 

[30] Dong, P., 2012. Editorial: Applications of light detection and ranging (LiDAR) in geosciences. Journal of Geology and Geosciences. 1:e102. doi:10.4172/jgg.1000e102.

 

[29] Zaragozí, B., Giménez, P., Navarro, J. T., Dong, P., Ramón, A., 2012. Development of free and opensource GIS software for cartographic generalization and occupancy area calculations. Ecological Informatics. Vol. 8, pp. 48-54.

 

[28] Wolverton, S., Nagaoka, L., Dong, P., and Kennedy, J., 2012. On behavioral depression in white-tailed deer. Journal of Archaeological Method and Theory.Vol.19. No.3, pp. 462-489.

 

[27] Dong, P., and Guo, H.D., 2012. A framework for automated assessment of post-earthquake building damage using geospatial data. International Journal of Remote Sensing. Vol. 33, No. 1, pp. 81-100.

 

[26] Tang, S., Dong, P., Buckles, B.P., 2012. A new method for extracting trees and buildings from sparse LiDAR data in urban areas. Remote Sensing Letters.[formerly Research Letters in the International Journal of Remote Sensing ]. Vol. 3, No. 3, pp. 211-219.

 

[25] Dong, P., and Lin, P., 2011. China: Teacher Preparation for GIS in the National Geography Curriculum. Book chapters in (Milson, Demirci, and Kerski, eds.) International Perspectives on Teaching and Learning with GIS in Secondary Schools. Springer.

 

[24] Dong, P., Ramesh, S., and Nepali, A., 2010. Evaluation of small area population estimation using LiDAR, Landsat TM and parcel data. International Journal of Remote Sensing. Vol. 31, No. 21, pp. 5571-5586.

 

[23] Tang, S., Dong, P., Buckles, B.P., 2010. Comparison of two classification methods for feature extraction from LiDAR data in urban areas. The 2010 International Conference on Image Processing, Computer Vision, & Pattern Recognition (IPCV'10), Las Vegas, Nevada, USA. July 12-15, 2010.(Peer-reviewed conference paper, acceptance rate: 28%).

 

[22] Dong, P., 2010. Sensitivity of LiDAR-derived three-dimensional shape signatures for individual tree crowns: A simulation study. Remote Sensing Letters. [formerly Research Letters in the International Journal of Remote Sensing ]. Vol. 1, No. 3, pp. 159-167.

 

[21] Dong, P., 2009. Characterization of individual tree crowns using three-dimensional shape signatures derived from LiDAR data. International Journal of Remote Sensing. Vol. 30, No. 24, pp. 6621-6628.

 

[20] Dong, P., 2009. Lacunarity analysis of raster datasets and 1D, 2D, and 3D point patterns. Computers and Geosciences. Vol. 35, pp. 2100-2110.

 

[19] Yang, X., Huang, Y., Dong, P., Jiang, D., and Liu, H., 2009. An updating system for the gridded population database of China based on remote sensing, GIS and spatial database technologies. Sensors. Vol. 9, No. 2, pp. 1128-1140.

 

[18] Dong, P., 2008. Generating and updating multiplicatively weighted Voronoidiagrams for point, line and polygon features in GIS. Computers and Geosciences. Vol. 34, pp. 411-421.

 

[17] Dong, P., 2008. Fractal signatures for multiscale processing of hyperspectralimage data. Advances in Space Research. Vol. 41, pp. 1733-1743.

 

[16] Huang, Y., Chen, C., and Dong, P., 2008. Modeling herds and their evolvements from trajectory data. GIScience 2008-the 5th International Conference on Geographic Information Science, Park City, Utah, September 23-26, 2008. (Peer-reviewed conference paper, acceptance rate: 24%).

 

[15] Dong, P., 2005. Development of a GIS/GPS Based Emergency Response System. Geomatica, Vol. 59, No. 4, pp. 427-434.

 

[14] Dong, P., and Leblon, B., 2004. Rock unit discrimination on Landsat-TM, SIR-C and RADARSAT images using spectral and textural information. International Journal of Remote Sensing, Vol. 25, No. 18, pp.3745-3768.

 

[13] Dong, P., 2000. Lacunarity for spatial heterogeneity measurement in GIS. Journal of Geographical Information Sciences, Vol. 6, No. 1, pp. 20-26. 

 

[12] Dong, P., 2000. Test of a new lacunarity estimation method for image texture analysis. International Journal of Remote Sensing. Vol. 21, No. 17, pp. 3369-3373.

 

[11] Dong, P., 1997. Implementation of mathematical morphological operations for spatial data processing. Computers and Geosciences, Vol. 23, No. 1, pp. 103-107.

 

[10] Dong, P., 1995. A test study on alteration mapping using Landsat TM data and geographic information system. in: (Guo et al. eds) Methodology and practice of remote sensing for mineral exploration, Science Press, Beijing. (in Chinese with English abstract), pp. 24-28.

 

[9] Dong, P., 1995. Integration of Landsat TM and airborne SAR imagery in QingheArea, Xinjiang. in: (Guo, ed.) Methodology and Practice of Remote Sensing for Mineral Exploration. Science Press, Beijing. (in Chinese with English abstract). pp. 49-53.

 

[8] Dong, P., and Guo, H.D., 1995. Multi-source geological data set analysis supported by GIS. in: (Guo et al. eds) Methodology and practice of remote sensing for mineral exploration, Science Press, Beijing. (in Chinese with English abstract), pp. 58-66.

 

[7] Dong, P., 1995. Trend surface analysis of the lineaments derived from remotely sensed imagery and its geological significance in eastern Altay region. in: (Guo, ed.) Methodology and Practice of Remote Sensing for Mineral Exploration. Science Press, Beijing. (in Chinese with English abstract). pp. 130-136.

 

[6] Dong, P., 1995. Integration of multi-source remotely sensed and ancillary data sets. Chapter 4 of (Guo ed.) Remote Sensing for Geological Mapping and Mineral Exploration in Northern Xinjiang. (in Chinese), Science Press, Beijing, pp. 45-52.

 

[5] Guo, H.D., Li, L., and Dong, P., 1995. Perspective of remote sensing techniques for geological mapping and mineral exploration. Chapter 12 of (Guo ed.) Remote sensing for Geological Mapping and Mineral Exploration in Northern Xinjiang. (in Chinese), Science Press, Beijing, pp. 230-246.

 

[4] Guo, H.D., Dong, P., Li, L., and Wang, J.D., 1993. Methodologies of remote sensing information extraction and multi-source data integration for mineral exploration. in: (G.Z. Tued.) Advances in Solid Earth Research of Xinjiang, China. Science Press, Beijing. (inChinese with English abstract). pp. 421-437.

 

[3] Guo, H.D., Zhang, Y.H., Shao, Y., Dong, P., and Wang, C., 1993. Geological analysis using shuttle imaging radar and airborne SAR in China. Advances in Space Research, Vol. 13, No. 11, pp. 79-82.

 

[2] Guo, H.D., and Dong, P., 1992. Integrated MSS-SAR-SPOT-Geophysical and Geochemical Data for Exploration Geology in Yeder Area. Advances in Space Research, Vol.12, No.7, pp. 27-30.

 

[1] Dong, P., 1992. Extraction and comparison of lineaments from synthetic aperture radar images with two illumination directions. in: (Guo et al. eds) Radar Image Analysis and Applications. Science Press, Beijing. (in Chinese with English abstract), pp. 36-41.