Estimation of rice leaf nitrogen contents based on hyperspectral LIDAR
暂无分享,去创建一个
Bo Zhu | Lin Du | Wei Gong | Shuo Shi | Jia Sun | Shalei Song | Jian Yang | W. Gong | Jian Yang | Jia Sun | S. Shi | L. Du | Shalei Song | Bo Zhu
[1] Teemu Hakala,et al. Fast and nondestructive method for leaf level chlorophyll estimation using hyperspectral LiDAR , 2014 .
[2] Z. Niu,et al. Estimation of leaf biochemical content using a novel hyperspectral full-waveform LiDAR system , 2014 .
[3] Anatoly A. Gitelson,et al. Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels , 2013, Int. J. Appl. Earth Obs. Geoinformation.
[4] Teemu Hakala,et al. Nitrogen concentration estimation with hyperspectral LiDAR , 2013 .
[5] Michele Dalponte,et al. Tree Species Classification in Boreal Forests With Hyperspectral Data , 2013, IEEE Transactions on Geoscience and Remote Sensing.
[6] Jon Atli Benediktsson,et al. Spectral Derivative Features for Classification of Hyperspectral Remote Sensing Images: Experimental Evaluation , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[7] Gong Wei,et al. Multi-wavelength canopy LiDAR for remote sensing of vegetation: Design and system performance , 2012 .
[8] J. Suomalainen,et al. Full waveform hyperspectral LiDAR for terrestrial laser scanning. , 2012, Optics express.
[9] Xin Huang,et al. Wavelength selection and spectral discrimination for paddy rice, with laboratory measurements of hyperspectral leaf reflectance , 2011 .
[10] Jungho Im,et al. Support vector machines in remote sensing: A review , 2011 .
[11] Pablo J. Zarco-Tejada,et al. Field characterization of olive (Olea europaea L.) tree crown architecture using terrestrial laser scanning data , 2011 .
[12] V. Alchanatis,et al. Review: Sensing technologies for precision specialty crop production , 2010 .
[13] Yuwei Chen,et al. Two-channel Hyperspectral LiDAR with a Supercontinuum Laser Source , 2010, Sensors.
[14] Jon Atli Benediktsson,et al. SVM- and MRF-Based Method for Accurate Classification of Hyperspectral Images , 2010, IEEE Geoscience and Remote Sensing Letters.
[15] A. Brenning. Benchmarking classifiers to optimally integrate terrain analysis and multispectral remote sensing in automatic rock glacier detection , 2009 .
[16] K. H. Ryu,et al. Fertiliser application performance of a variable-rate pneumatic granular applicator for rice production , 2008 .
[17] Benyang Tang,et al. Spacebased Estimation of Moisture Transport in Marine Atmosphere Using Support Vector Regression , 2008 .
[18] P. Varshney,et al. Multisource Classification Using Support Vector Machines: An Empirical Comparison with Decision Tree and Neural Network Classifiers , 2008 .
[19] Weixing Cao,et al. Analysis of common canopy vegetation indices for indicating leaf nitrogen accumulations in wheat and rice , 2008, Int. J. Appl. Earth Obs. Geoinformation.
[20] M. Borengasser,et al. Hyperspectral Remote Sensing: Principles and Applications , 2007 .
[21] Juha Hyyppä,et al. Toward Hyperspectral Lidar: Measurement of Spectral Backscatter Intensity With a Supercontinuum Laser Source , 2007, IEEE Geoscience and Remote Sensing Letters.
[22] S. Durbha,et al. Support vector machines regression for retrieval of leaf area index from multiangle imaging spectroradiometer , 2007 .
[23] K. Itten,et al. Fusion of imaging spectrometer and LIDAR data over combined radiative transfer models for forest canopy characterization , 2007 .
[24] Gustavo Camps-Valls,et al. Retrieval of oceanic chlorophyll concentration with relevance vector machines , 2006 .
[25] K. Itten,et al. Estimation of LAI and fractional cover from small footprint airborne laser scanning data based on gap fraction , 2006 .
[26] José Luis Rojo-Álvarez,et al. Robust support vector regression for biophysical variable estimation from remotely sensed images , 2006, IEEE Geoscience and Remote Sensing Letters.
[27] Y. Ito,et al. High contrast and efficient blazed grating light valve for full-HD 5,000 lumen laser projectors , 2006, 2006 Digest of Technical Papers International Conference on Consumer Electronics.
[28] H. Cai,et al. MEMS Littman tunable laser using curve-shaped blazed grating , 2005, The 13th International Conference on Solid-State Sensors, Actuators and Microsystems, 2005. Digest of Technical Papers. TRANSDUCERS '05..
[29] Mingyi He,et al. Band selection based on feature weighting for classification of hyperspectral data , 2005, IEEE Geoscience and Remote Sensing Letters.
[30] Gabriele Moser,et al. Partially Supervised classification of remote sensing images through SVM-based probability density estimation , 2005, IEEE Transactions on Geoscience and Remote Sensing.
[31] Lorenzo Bruzzone,et al. Robust multiple estimator systems for the analysis of biophysical parameters from remotely sensed data , 2005, IEEE Transactions on Geoscience and Remote Sensing.
[32] D. Civco,et al. Road Extraction Using SVM and Image Segmentation , 2004 .
[33] Giles M. Foody,et al. Toward intelligent training of supervised image classifications: directing training data acquisition for SVM classification , 2004 .
[34] Ranga B. Myneni,et al. Lidar remote sensing for modeling gross primary production of deciduous forests , 2004 .
[35] Songxin Tan,et al. Design and performance of a multiwavelength airborne polarimetric lidar for vegetation remote sensing. , 2004, Applied optics.
[36] Paul M. Mather,et al. An assessment of the effectiveness of decision tree methods for land cover classification , 2003 .
[37] J. Schjoerring,et al. Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression , 2003 .
[38] N. Zhang,et al. Precision agriculture—a worldwide overview , 2002 .
[39] J. Peñuelas,et al. Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data: Decomposing biochemical from structural signals , 2002 .
[40] Eleazar Eskin,et al. The Spectrum Kernel: A String Kernel for SVM Protein Classification , 2001, Pacific Symposium on Biocomputing.
[41] M. Lefsky,et al. Laser altimeter canopy height profiles: methods and validation for closed-canopy, broadleaf forests , 2001 .
[42] 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 .
[43] Jean-Philippe Gastellu-Etchegorry,et al. A modeling approach to assess the robustness of spectrometric predictive equations for canopy chemistry , 2001 .
[44] Aloysius Wehr,et al. Airborne laser scanning—an introduction and overview , 1999 .
[45] C. Brodley,et al. Decision tree classification of land cover from remotely sensed data , 1997 .
[46] Fuan Tsai,et al. Derivative analysis of hyperspectral data for detecting spectral features , 1997, IGARSS'97. 1997 IEEE International Geoscience and Remote Sensing Symposium Proceedings. Remote Sensing - A Scientific Vision for Sustainable Development.
[47] L. Johnson,et al. Spectrometric Estimation of Total Nitrogen Concentration in Douglas-Fir Foliage , 1996 .
[48] S. Ustin,et al. Critique of stepwise multiple linear regression for the extraction of leaf biochemistry information from leaf reflectance data , 1996 .
[49] B. Yoder,et al. Predicting nitrogen and chlorophyll content and concentrations from reflectance spectra (400–2500 nm) at leaf and canopy scales , 1995 .
[50] Michael D. Steven,et al. High resolution derivative spectra in remote sensing , 1990 .
[51] Piech Ma,et al. Symbolic representation of hyperspectral data , 1987 .
[52] C. Tucker. Red and photographic infrared linear combinations for monitoring vegetation , 1979 .
[53] Armand Wirgin,et al. Theoretical and Experimental Investigation of a New Type of Blazed Grating , 1969 .
[54] F. Kneubühl,et al. Diffraction grating spectroscopy. , 1969, Applied optics.
[55] Teemu Hakala,et al. Artificial target detection with a hyperspectral LiDAR over 26-h measurement , 2015 .
[56] 龚威 Gong Wei,et al. A Method to Select Receiving Channels for the Multi-Spectral Earth Observation LiDAR , 2014 .
[57] L. Plümer,et al. Sequential support vector machine classification for small-grain weed species discrimination with special regard to Cirsium arvense and Galium aparine , 2012 .
[58] Zhang Qiheng. Artificial target detection based on enhanced fractal feature , 2006 .
[59] Jason A. Cole,et al. Hyperspectral Remote Sensing and Its Applications , 2005 .
[60] Douglas C. Schmidt,et al. The Design and Performance of , 2003 .
[61] G. A. Blackburn,et al. Spectral indices for estimating photosynthetic pigment concentrations: A test using senescent tree leaves , 1998 .
[62] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[63] J. Melack,et al. Remote sensing of foliar chemistry of inundated rice with imaging spectrometry , 1996 .
[64] A. Gitelson,et al. Detection of Red Edge Position and Chlorophyll Content by Reflectance Measurements Near 700 nm , 1996 .
[65] J. Mavor,et al. Digest of Technical Papers ESSCIRC'87 , 1987 .
[66] M. Piech,et al. Symbolic representation of hyperspectral data. , 1987, Applied optics.
[67] W. Heine,et al. [A century of Kjeldahl's nitrogen determination]. , 1985, Zeitschrift fur medizinische Laboratoriumsdiagnostik.
[68] R. Engelbrecht,et al. DIGEST of TECHNICAL PAPERS , 1959 .