Leaf Area Index Estimation Algorithm for GF-5 Hyperspectral Data Based on Different Feature Selection and Machine Learning Methods
暂无分享,去创建一个
Jinhui Lan | Lei Wang | Bing Wang | Kun Jia | Xiangqin Wei | Yunjun Yao | Xiang Zhao | Yuan Sun | Dandan Wei | Chenchao Xiao | Zhulin Chen | Xiang Zhao | K. Jia | Yunjun Yao | Lei Wang | C. Xiao | B. Wang | J. Lan | Zhulin Chen | D. Wei | Yuan Sun | Xiangqin Wei | X. Wei
[1] L. Alonso,et al. A red-edge spectral index for remote sensing estimation of green LAI over agroecosystems , 2013 .
[2] Neal W. Aven,et al. Mapping urban tree species using integrated airborne hyperspectral and LiDAR remote sensing data , 2017 .
[3] José Moreno,et al. Multi-Crop Green LAI Estimation with a New Simple Sentinel-2 LAI Index (SeLI) , 2019, Sensors.
[4] A. Skidmore,et al. Leaf Area Index derivation from hyperspectral vegetation indicesand the red edge position , 2009 .
[5] Evelyne Vigneau,et al. Random forests: A machine learning methodology to highlight the volatile organic compounds involved in olfactory perception , 2018, Food Quality and Preference.
[6] Ning Zeng,et al. Sensitivity of Tropical Land Climate to Leaf Area Index: Role of Surface Conductance versus Albedo* , 2004 .
[7] D. Roberts,et al. Using Imaging Spectroscopy to Study Ecosystem Processes and Properties , 2004 .
[8] Xiang Zhao,et al. Generating High Spatio-Temporal Resolution Fractional Vegetation Cover by Fusing GF-1 WFV and MODIS Data , 2019, Remote. Sens..
[9] Sepideh Karimi,et al. Generalizability of gene expression programming and random forest methodologies in estimating cropland and grassland leaf area index , 2018, Comput. Electron. Agric..
[10] Eileen M. Perry,et al. Spectral and spatial differences in response of vegetation indices to nitrogen treatments on apple , 2007 .
[11] C. Atzberger,et al. Spatially constrained inversion of radiative transfer models for improved LAI mapping from future Sentinel-2 imagery , 2012 .
[12] Xinyu Wu,et al. Dimensionality reduction of data sequences for human activity recognition , 2016, Neurocomputing.
[13] T. Jarmer,et al. Comparison of different regression models and validation techniques for the assessment of wheat leaf area index from hyperspectral data , 2015 .
[14] Peter Reinartz,et al. On the use of Sentinel-2 for coastal habitat mapping and satellite-derived bathymetry estimation using downscaled coastal aerosol band , 2019, Int. J. Appl. Earth Obs. Geoinformation.
[15] Ming Xu,et al. Estimating Yellow Starthistle (Centaurea solstitialis) Leaf Area Index and Aboveground Biomass with the Use of Hyperspectral Data , 2007, Weed Science.
[16] Xiaoxia Wang,et al. Comparison of Four Machine Learning Methods for Generating the GLASS Fractional Vegetation Cover Product from MODIS Data , 2016, Remote. Sens..
[17] Jiye Liang,et al. An efficient instance selection algorithm for k nearest neighbor regression , 2017, Neurocomputing.
[18] Seyed Mohammad Mirjalili,et al. Whale optimization approaches for wrapper feature selection , 2018, Appl. Soft Comput..
[19] F. Baret,et al. Review of methods for in situ leaf area index (LAI) determination: Part II. Estimation of LAI, errors and sampling , 2004 .
[20] Philip Lewis,et al. Variability and bias in active and passive ground-based measurements of effective plant, wood and leaf area index , 2018 .
[21] Tim R. McVicar,et al. Preprocessing EO-1 Hyperion hyperspectral data to support the application of agricultural indexes , 2003, IEEE Trans. Geosci. Remote. Sens..
[22] P. Thenkabail,et al. Hyperspectral Vegetation Indices and Their Relationships with Agricultural Crop Characteristics , 2000 .
[23] Yadan Zhang,et al. Non-invasive continuous blood pressure measurement based on mean impact value method, BP neural network, and genetic algorithm , 2018, Technology and health care : official journal of the European Society for Engineering and Medicine.
[24] Gustau Camps-Valls,et al. Mapping Leaf Area Index With a Smartphone and Gaussian Processes , 2015, IEEE Geoscience and Remote Sensing Letters.
[25] Jean-Michel Poggi,et al. Variable selection using random forests , 2010, Pattern Recognit. Lett..
[26] S. Leblanc,et al. Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements , 2002 .
[27] Gustau Camps-Valls,et al. Hyperspectral dimensionality reduction for biophysical variable statistical retrieval , 2017 .
[28] Ranga B. Myneni,et al. Stochastic transport theory for investigating the three-dimensional canopy structure from space measurements , 2008 .
[29] Petri Pellikka,et al. Utility of hyperspectral compared to multispectral remote sensing data in estimating forest biomass and structure variables in Finnish boreal forest , 2019, Int. J. Appl. Earth Obs. Geoinformation.
[30] Wolfram Mauser,et al. Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study , 2018, Remote. Sens..
[31] Marie Clément,et al. Applications of random forest feature selection for fine‐scale genetic population assignment , 2017, Evolutionary applications.
[32] J. Dungan,et al. Generating global Leaf Area Index from Landsat: Algorithm formulation and demonstration , 2012 .
[33] Robert Hecht-Nielsen. III.3 – Theory of the Backpropagation Neural Network* , 1992 .
[34] D.M. Mount,et al. An Efficient k-Means Clustering Algorithm: Analysis and Implementation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..
[35] Jonas Sjöberg,et al. Efficient training of neural nets for nonlinear adaptive filtering using a recursive Levenberg-Marquardt algorithm , 2000, IEEE Trans. Signal Process..
[36] Jindi Wang,et al. Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance , 2014, IEEE Transactions on Geoscience and Remote Sensing.
[37] Zaheer Iqbal,et al. Remote Sensing-Based Mapping of Senescent Leaf C: N Ratio in the Sundarbans Reserved Forest Using Machine Learning Techniques , 2020, Remote. Sens..
[38] Shunlin Liang,et al. Evaluation of four long time-series global leaf area index products , 2017 .
[39] Roberta E. Martin,et al. Multi-method ensemble selection of spectral bands related to leaf biochemistry , 2015 .
[40] Hankui K. Zhang,et al. Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data , 2013 .
[41] P. D. Heermann,et al. Classification of multispectral remote sensing data using a back-propagation neural network , 1992, IEEE Trans. Geosci. Remote. Sens..
[42] Lei Wang,et al. Assimilation of the leaf area index and vegetation temperature condition index for winter wheat yield estimation using Landsat imagery and the CERES-Wheat model , 2017 .
[43] Xiangdong Lei,et al. Individual Tree Diameter Growth Models of Larch–Spruce–Fir Mixed Forests Based on Machine Learning Algorithms , 2019, Forests.
[44] Dong Li,et al. Combined Use of Airborne LiDAR and Satellite GF-1 Data to Estimate Leaf Area Index, Height, and Aboveground Biomass of Maize During Peak Growing Season , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[45] Marco Heurich,et al. Mapping leaf chlorophyll content from Sentinel-2 and RapidEye data in spruce stands using the invertible forest reflectance model , 2019, Int. J. Appl. Earth Obs. Geoinformation.
[46] Lorenzo Bruzzone,et al. Feature Selection Based on High Dimensional Model Representation for Hyperspectral Images , 2017, IEEE Transactions on Image Processing.
[47] John Shepanski,et al. Hyperion, a space-based imaging spectrometer , 2003, IEEE Trans. Geosci. Remote. Sens..
[48] Hongxing Liu,et al. Exploring the Potential of WorldView-2 Red-Edge Band-Based Vegetation Indices for Estimation of Mangrove Leaf Area Index with Machine Learning Algorithms , 2017, Remote. Sens..
[49] Michel Verleysen,et al. Kernel-based dimensionality reduction using Renyi's α-entropy measures of similarity , 2017, Neurocomputing.
[50] Xiaoyu Song,et al. Exploring the Best Hyperspectral Features for LAI Estimation Using Partial Least Squares Regression , 2014, Remote. Sens..
[51] Helmi Zulhaidi Mohd Shafri,et al. Spectral feature selection and classification of roofing materials using field spectroscopy data , 2015 .
[52] Alfred Lenin Fred,et al. AC coefficient and K-means cuckoo optimisation algorithm-based segmentation and compression of compound images , 2018, IET Image Process..
[53] Bing Wang,et al. Assessment of Sentinel-2 MSI Spectral Band Reflectances for Estimating Fractional Vegetation Cover , 2018, Remote. Sens..
[54] Wolfram Mauser,et al. Improvements of plant parameter estimations with hyperspectral data compared to multispectral data , 1997, Remote Sensing.
[55] Clement Atzberger,et al. LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements , 2008 .
[56] Chih-Jen Lin,et al. Large-Scale Linear RankSVM , 2014, Neural Computation.
[57] Quan Sun,et al. ractional vegetation cover estimation in arid and semi-arid environments using J-1 satellite hyperspectral data , 2012 .
[58] Fang Liu,et al. Unsupervised feature selection based on maximum information and minimum redundancy for hyperspectral images , 2016, Pattern Recognit..
[59] Guang Jin,et al. Research on power energy load forecasting method based on KNN , 2019, International Journal of Ambient Energy.
[60] Jan G. P. W. Clevers,et al. Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties - A review , 2015 .
[61] Yilin Zhao,et al. The Design and Implementation of Intrusion Detection System based on Data Mining Technology , 2013 .
[62] Damaris Zurell,et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance , 2013 .
[63] Benjamin Munson,et al. Supervised and unsupervised machine learning approaches to classifying chimpanzee vocalizations , 2018 .
[64] Li Li,et al. Monitoring maize growth conditions by training a BP neural network with remotely sensed vegetation temperature condition index and leaf area index , 2019, Comput. Electron. Agric..
[65] O. Hagolle,et al. LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION: Part 1: Principles of the algorithm , 2007 .
[66] M. Ashton,et al. Hyperion, IKONOS, ALI, and ETM+ sensors in the study of African rainforests , 2004 .
[67] Tin Kam Ho,et al. The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[68] Kit Yan Chan,et al. Main Effect Fine-tuning of the Mutation Operator and the Neighbourhood Function for Uncapacitated Facility Location Problems , 2006, Soft Comput..
[69] M. Ashton,et al. Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications , 2004 .
[70] W. Cohen,et al. Hyperspectral versus multispectral data for estimating leaf area index in four different biomes , 2004 .
[71] Tao Yu,et al. Leaf Area Index Estimation Using Chinese GF-1 Wide Field View Data in an Agriculture Region , 2017, Sensors.
[72] Hitendra Padalia,et al. Evaluation of the Use of Hyperspectral Vegetation Indices for Estimating Mangrove Leaf Area Index in Middle Andaman Island, India , 2018, Remote Sensing Letters.
[73] Mario Cunha,et al. Retrieval of Maize Leaf Area Index Using Hyperspectral and Multispectral Data , 2018, Remote. Sens..
[74] Jing Liu,et al. Improving leaf area index (LAI) estimation by correcting for clumping and woody effects using terrestrial laser scanning , 2018, Agricultural and Forest Meteorology.
[75] O. Mutanga,et al. Estimating LAI and mapping canopy storage capacity for hydrological applications in wattle infested ecosystems using Sentinel-2 MSI derived red edge bands , 2018, GIScience & Remote Sensing.
[76] Michel Verleysen,et al. Nonlinear dimensionality reduction of data manifolds with essential loops , 2005, Neurocomputing.
[77] R. Colombo,et al. Inversion of a radiative transfer model with hyperspectral observations for LAI mapping in poplar plantations , 2004 .
[78] Jing M. Chen,et al. A robust leaf area index algorithm accounting for the expected errors in gap fraction observations , 2018 .
[79] Bing Zhang,et al. A novel two-step method for winter wheat-leaf chlorophyll content estimation using a hyperspectral vegetation index , 2014 .
[80] W. Verhoef,et al. PROSPECT+SAIL models: A review of use for vegetation characterization , 2009 .
[81] P. Starks,et al. Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.
[82] Frédéric Baret,et al. Fractional vegetation cover estimation algorithm for Chinese GF-1 wide field view data , 2016 .
[83] A. Skidmore,et al. Mapping grassland leaf area index with airborne hyperspectral imagery : a comparison study of statistical approaches and inversion of radiative transfer models , 2011 .
[84] Xiaohuan Xi,et al. Retrieving aboveground biomass of wetland Phragmites australis (common reed) using a combination of airborne discrete-return LiDAR and hyperspectral data , 2017, Int. J. Appl. Earth Obs. Geoinformation.
[85] Mohammad Reza Keyvanpour,et al. A New Feature Selection Method Based on Ant Colony and Genetic Algorithm on Persian Font Recognition , 2012 .
[86] Samuel Corgne,et al. Agricultural practices in grasslands detected by spatial remote sensing , 2014, Environmental Monitoring and Assessment.
[87] Zhongguang Fu,et al. Research on a feature selection method based on median impact value for modeling in thermal power plants , 2016 .
[88] Jan G. P. W. Clevers,et al. Experimental Sentinel-2 LAI estimation using parametric, non-parametric and physical retrieval methods - A comparison , 2015 .
[89] S. Ollinger,et al. DIRECT ESTIMATION OF ABOVEGROUND FOREST PRODUCTIVITY THROUGH HYPERSPECTRAL REMOTE SENSING OF CANOPY NITROGEN , 2002 .
[90] Siti Khairunniza-Bejo,et al. A comparative study on dimensionality reduction of dielectric spectral data for the classification of basal stem rot (BSR) disease in oil palm , 2020, Comput. Electron. Agric..
[91] Roberta E. Martin,et al. Airborne spectranomics: mapping canopy chemical and taxonomic diversity in tropical forests , 2009 .
[92] Thomas Jarmer,et al. High-Resolution UAV-Based Hyperspectral Imagery for LAI and Chlorophyll Estimations from Wheat for Yield Prediction , 2018, Remote. Sens..
[93] Yingying Dong,et al. Retrieval of crop biophysical parameters from Sentinel-2 remote sensing imagery , 2019, Int. J. Appl. Earth Obs. Geoinformation.
[94] Li Wang,et al. Estimation of paddy rice leaf area index using machine learning methods based on hyperspectral data from multi-year experiments , 2018, PloS one.
[95] Lifeng Xi,et al. Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods , 2007 .
[96] Zhuang Wang,et al. Scaling Up Kernel SVM on Limited Resources: A Low-Rank Linearization Approach , 2012, IEEE Transactions on Neural Networks and Learning Systems.
[97] Viswanathan Chinnusamy,et al. Comparative analysis of index and chemometric techniques-based assessment of leaf area index (LAI) in wheat through field spectroradiometer, Landsat-8, Sentinel-2 and Hyperion bands , 2020, Geocarto International.
[98] Deepak Khazanchi,et al. Optimizing Feature Selection Using Particle Swarm Optimization and Utilizing Ventral Sides of Leaves for Plant Leaf Classification , 2016 .
[99] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[100] K. Soudani,et al. Comparative analysis of IKONOS, SPOT, and ETM+ data for leaf area index estimation in temperate coniferous and deciduous forest stands , 2006 .
[101] A. Stewart Fotheringham,et al. Geographically weighted regression and multicollinearity: dispelling the myth , 2016, J. Geogr. Syst..