Multispectral LiDAR Point Cloud Classification: A Two-Step Approach

Target classification techniques using spectral imagery and light detection and ranging (LiDAR) are widely used in many disciplines. However, none of the existing methods can directly capture spectral and 3D spatial information simultaneously. Multispectral LiDAR was proposed to solve this problem as its data combines spectral and 3D spatial information. Point-based classification experiments have been conducted with the use of multispectral LiDAR; however, the low signal to noise ratio creates salt and pepper noise in the spectral-only classification, thus lowering overall classification accuracy. In our study, a two-step classification approach is proposed to eliminate this noise during target classification: routine classification based on spectral information using spectral reflectance or a vegetation index, followed by neighborhood spatial reclassification. In an experiment, a point cloud was first classified with a routine classifier using spectral information and then reclassified with the k-nearest neighbors (k-NN) algorithm using neighborhood spatial information. Next, a vegetation index (VI) was introduced for the classification of healthy and withered leaves. Experimental results show that our proposed two-step classification method is feasible if the first spectral classification accuracy is reasonable. After the reclassification based on the k-NN algorithm was combined with neighborhood spatial information, accuracies increased by 1.50–11.06%. Regarding identification of withered leaves, VI performed much better than raw spectral reflectance, with producer accuracy increasing from 23.272% to 70.507%.

[1]  Peter T. Wolter,et al.  Improved forest classification in the northern Lake States using multi-temporal Landsat imagery , 1995 .

[2]  F. Baret,et al.  HIGH SPEcrRAL RESOLUTION : DETERMINATION OF SPEcrRAL SHIFTS BETWEEN THE RED AND INFRARED , 2012 .

[3]  Gionata Biavati,et al.  Correction scheme for close-range lidar returns. , 2011, Applied optics.

[4]  Wen Liu,et al.  Learning Change from Synthetic Aperture Radar Images: Performance Evaluation of a Support Vector Machine to Detect Earthquake and Tsunami-Induced Changes , 2016, Remote. Sens..

[5]  Andrew M. Wallace,et al.  Recovery of Forest Canopy Parameters by Inversion of Multispectral LiDAR Data , 2012, Remote. Sens..

[6]  Jocelyn Chanussot,et al.  Decision Fusion for the Classification of Hyperspectral Data: Outcome of the 2008 GRS-S Data Fusion Contest , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[7]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[8]  Wenjiang Huang,et al.  [New index for crop canopy fresh biomass estimation]. , 2010, Guang pu xue yu guang pu fen xi = Guang pu.

[9]  Lorenzo Bruzzone,et al.  A Composite Semisupervised SVM for Classification of Hyperspectral Images , 2009, IEEE Geoscience and Remote Sensing Letters.

[10]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[11]  Evaristo Ricchetti,et al.  Multispectral Satellite Image and Ancillary Data Integration for Geological Classification , 2000 .

[12]  Cathleen E. Jones,et al.  Structural Classification of Marshes with Polarimetric SAR Highlighting the Temporal Mapping of Marshes Exposed to Oil , 2015, Remote. Sens..

[13]  Timothy J. Malthus,et al.  A Multispectral Canopy LiDAR Demonstrator Project , 2011, IEEE Geoscience and Remote Sensing Letters.

[14]  F. M. Danson,et al.  Multispectral and LiDAR data fusion for fuel type mapping using Support Vector Machine and decision rules , 2011 .

[15]  A. Solberg,et al.  Oil spill detection by satellite remote sensing , 2005 .

[16]  Jixian Zhang,et al.  Advances in fusion of optical imagery and LiDAR point cloud applied to photogrammetry and remote sensing , 2017 .

[17]  Bo Zhu,et al.  Investigating the Potential of Using the Spatial and Spectral Information of Multispectral LiDAR for Object Classification , 2015, Sensors.

[18]  Wei Gong,et al.  Accurate identification of nitrogen fertilizer application of paddy rice using laser-induced fluorescence combined with support vector machine , 2016 .

[19]  Wei Gong,et al.  Application of fluorescence spectrum to precisely inverse paddy rice nitrogen content , 2016 .

[20]  Z. Niu,et al.  Estimation of leaf biochemical content using a novel hyperspectral full-waveform LiDAR system , 2014 .

[21]  Andreas Zell,et al.  3D LIDAR- and Camera-Based Terrain Classification Under Different Lighting Conditions , 2012, AMS.

[22]  Tao Tang,et al.  Accuracy test of point-based and object-based urban building feature classification and extraction applying airborne LiDAR data , 2014 .

[23]  Dianjun Zhang,et al.  Manifold Learning Co-Location Decision Tree for Remotely Sensed Imagery Classification , 2016, Remote. Sens..

[24]  J. Dungan,et al.  Visualizing Distributions from Multi-Return Lidar Data to Understand Forest Structure , 2005 .

[25]  B. Koch,et al.  Detection of individual tree crowns in airborne lidar data , 2006 .

[26]  P. Litkey,et al.  TOWARDS AUTOMATIC SINGLE-SENSOR MAPPING BY MULTISPECTRAL AIRBORNE LASER SCANNING , 2016 .

[27]  B. Wardlow,et al.  Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains , 2007 .

[28]  A. Gitelson,et al.  Use of a green channel in remote sensing of global vegetation from EOS- MODIS , 1996 .

[29]  V. Wichmann,et al.  EVALUATING THE POTENTIAL OF MULTISPECTRAL AIRBORNE LIDAR FOR TOPOGRAPHIC MAPPING AND LAND COVER CLASSIFICATION , 2015 .

[30]  Gong Wei,et al.  Multi-wavelength canopy LiDAR for remote sensing of vegetation: Design and system performance , 2012 .

[31]  J. Brasington,et al.  Object-based land cover classification using airborne LiDAR , 2008 .

[32]  Juha Hyyppä,et al.  Classification of Spruce and Pine Trees Using Active Hyperspectral LiDAR , 2013, IEEE Geoscience and Remote Sensing Letters.

[33]  Li Wang,et al.  Design of a New Multispectral Waveform LiDAR Instrument to Monitor Vegetation , 2015, IEEE Geoscience and Remote Sensing Letters.

[34]  W. E. Larson,et al.  Coincident detection of crop water stress, nitrogen status and canopy density using ground-based multispectral data. , 2000 .

[35]  Cheng Wang,et al.  Using Stacked Generalization to Combine SVMs in Magnitude and Shape Feature Spaces for Classification of Hyperspectral Data , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[36]  N. Broge,et al.  Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density , 2001 .

[37]  Jie Wang,et al.  Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery , 2014, Remote. Sens..

[38]  Lorenzo Bruzzone,et al.  An experimental comparison of neural and statistical non-parametric algorithms for supervised classification of remote-sensing images , 1996, Pattern Recognit. Lett..

[39]  Guihua Zhao,et al.  3D LAND COVER CLASSIFICATION BASED ON MULTISPECTRAL LIDAR POINT CLOUDS , 2016 .

[40]  Teemu Hakala,et al.  Fast and nondestructive method for leaf level chlorophyll estimation using hyperspectral LiDAR , 2014 .

[41]  Bo Zhu,et al.  Improving Backscatter Intensity Calibration for Multispectral LiDAR , 2015, IEEE Geoscience and Remote Sensing Letters.

[42]  Jungho Im,et al.  Support vector machines in remote sensing: A review , 2011 .

[43]  Gong Wei,et al.  A Multi-Wavelength Canopy LiDAR for Vegetation Monitoring: System Implementation and Laboratory-Based Tests , 2011 .

[44]  Eric C. Turnblom,et al.  Tree Species Detection Accuracies Using Discrete Point Lidar and Airborne Waveform Lidar , 2012, Remote. Sens..

[45]  Moon S. Kim,et al.  The use of high spectral resolution bands for estimating absorbed photosynthetically active radiation (A par) , 1994 .

[46]  Shuhab D. Khan,et al.  Application of multispectral LiDAR to automated virtual outcrop geology , 2014 .

[47]  Moon S. Kim,et al.  Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance , 2000 .

[48]  D. Walvoort,et al.  A linear model to predict with a multi‐spectral radiometer the amount of nitrogen in winter wheat , 2006 .

[49]  A. Huete.  A soil-adjusted vegetation index (SAVI) , 1988 .

[50]  K. Tempfli,et al.  Building extraction from laser data by reasoning on image segments in elevation slices , 2002 .

[51]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[52]  Hartmut K. Lichtenthaler,et al.  The Chlorophyll Fluorescence Ratio F735/F700 as an Accurate Measure of the Chlorophyll Content in Plants , 1999 .

[53]  Begüm Demir,et al.  Clustering-Based Extraction of Border Training Patterns for Accurate SVM Classification of Hyperspectral Images , 2009, IEEE Geoscience and Remote Sensing Letters.

[54]  G. Georgiev,et al.  Long-term calibration monitoring of Spectralon diffusers BRDF in the air-ultraviolet. , 2007, Applied optics.

[55]  Clement Atzberger,et al.  Tree Species Classification with Random Forest Using Very High Spatial Resolution 8-Band WorldView-2 Satellite Data , 2012, Remote. Sens..

[56]  Samia Boukir,et al.  Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests , 2011 .

[57]  N. Altman.  An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression , 1992 .

[58]  Wei Gong,et al.  Estimation of nitrogen content based on fluorescence spectrum and principal component analysis in paddy rice. , 2016 .

[59]  Dimitri Lague,et al.  3D Terrestrial LiDAR data classification of complex natural scenes using a multi-scale dimensionality criterion: applications in geomorphology , 2011, ArXiv.

[60]  John R. Miller,et al.  Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture , 2004 .

[61]  Cheng Wang,et al.  Fusion of SVMs in wavelet domain for hyperspectral data classification , 2009, 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[62]  John R. Miller,et al.  Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture , 2002 .

[63]  G. Rondeaux,et al.  Optimization of soil-adjusted vegetation indices , 1996 .

[64]  Bo Zhu,et al.  Estimation of rice leaf nitrogen contents based on hyperspectral LIDAR , 2016, Int. J. Appl. Earth Obs. Geoinformation.