Crop Type Discrimination and Health Assessment using Hyperspectral Imaging
暂无分享,去创建一个
Rajsi Kot | Susan Ustin | Rojalin Tripathy | Rahul Nigam | Sujay Dutta | Nita Bhagia | Bimal K. Bhattacharya | S. Ustin | S. Dutta | B. Bhattacharya | R. Nigam | R. Tripathy | K. Chandrasekar | R. Nagori | Rohit Nagori | K. Chandrasekar | N. Bhagia | R. Kot
[1] I. Sandholt,et al. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status , 2002 .
[2] Davoud Ashourloo,et al. Developing Two Spectral Disease Indices for Detection of Wheat Leaf Rust (Pucciniatriticina) , 2014, Remote. Sens..
[3] Gang Wang,et al. Deep Learning-Based Classification of Hyperspectral Data , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[4] Roberta E. Martin,et al. Spectral and chemical analysis of tropical forests: Scaling from leaf to canopy levels , 2008 .
[5] J. A. Schell,et al. Monitoring vegetation systems in the great plains with ERTS , 1973 .
[6] C. Yonezawa. Maximum likelihood classification combined with spectral angle mapper algorithm for high resolution satellite imagery , 2007 .
[7] T. Jackson,et al. Vegetation water content estimation for corn and soybeans using spectral indices derived from MODIS near- and short-wave infrared bands , 2005 .
[8] D. P. Turner,et al. Scaling net primary production to a MODIS footprint in support of Earth observing system product validation , 2004 .
[9] Nataliia Kussul,et al. Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data , 2017, IEEE Geoscience and Remote Sensing Letters.
[10] Jessica A. Faust,et al. Imaging Spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) , 1998 .
[11] Lorenzo Bruzzone,et al. Classification of hyperspectral remote sensing images with support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.
[12] A. Gitelson,et al. Assessing Carotenoid Content in Plant Leaves with Reflectance Spectroscopy¶ , 2002, Photochemistry and photobiology.
[13] Pablo J. Zarco-Tejada,et al. Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle (UAV) , 2013 .
[14] Jun Li,et al. Regional Clustering-Based Spatial Preprocessing for Hyperspectral Unmixing , 2018 .
[15] Robert A. Schowengerdt,et al. A review and analysis of backpropagation neural networks for classification of remotely-sensed multi-spectral imagery , 1995 .
[16] M. Ashton,et al. Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications , 2004 .
[17] Simon J. Hook,et al. Linking seasonal foliar traits to VSWIR-TIR spectroscopy across California ecosystems , 2016 .
[18] J. M. Bremner. Determination of nitrogen in soil by the Kjeldahl method , 1960, The Journal of Agricultural Science.
[19] D. Tanré,et al. Strategy for direct and indirect methods for correcting the aerosol effect on remote sensing: From AVHRR to EOS-MODIS , 1996 .
[20] A. Huete,et al. A comparison of vegetation indices over a global set of TM images for EOS-MODIS , 1997 .
[21] C. Field,et al. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency , 1992 .
[22] F. M. Danson,et al. Extraction of vegetation biophysical parameters by inversion of the PROSPECT + SAIL models on sugar beet canopy reflectance data. Application to TM and AVIRIS sensors , 1995 .
[23] Ulrich H.-G. Kreßel,et al. Pairwise classification and support vector machines , 1999 .
[24] B. Gao. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space , 1996 .
[25] W. Verhoef,et al. PROSPECT+SAIL models: A review of use for vegetation characterization , 2009 .
[26] S. J. Sutley,et al. Imaging spectroscopy: Earth and planetary remote sensing with the USGS Tetracorder and expert systems , 2003 .
[27] C. Woodcock,et al. Resolution dependent errors in remote sensing of cultivated areas , 2006 .
[28] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[29] M. Hardisky. The Influence of Soil Salinity, Growth Form, and Leaf Moisture on-the Spectral Radiance of Spartina alterniflora Canopies , 2008 .
[30] Christopher B. Field,et al. Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves☆ , 1994 .
[31] Paul V. Bolstad,et al. Semi-automated training approaches for spectral class definition , 1992 .
[32] Wei Huang,et al. A Three-Dimensional Variational Data Assimilation System for MM5: Implementation and Initial Results , 2004 .
[33] D. Sims,et al. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages , 2002 .
[34] N. Elliott,et al. Reflectance characteristics of Russian wheat aphid (Hemiptera: Aphididae) stress and abundance in winter wheat , 2007 .
[35] P. Switzer,et al. A transformation for ordering multispectral data in terms of image quality with implications for noise removal , 1988 .
[36] D. M. Moss,et al. Red edge spectral measurements from sugar maple leaves , 1993 .
[37] C. Brodley,et al. Decision tree classification of land cover from remotely sensed data , 1997 .
[38] G. Birth,et al. Measuring the Color of Growing Turf with a Reflectance Spectrophotometer1 , 1968 .
[39] J. Peñuelas,et al. Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data: Decomposing biochemical from structural signals , 2002 .
[40] A. R. Harrison,et al. Standardized principal components , 1985 .
[41] A. Gitelson,et al. Novel algorithms for remote estimation of vegetation fraction , 2002 .
[42] W. R. Windham,et al. Exploring the relationship between reflectance red edge and chlorophyll concentration in slash pine leaves. , 1995, Tree physiology.
[43] Manuel A. Aguilar,et al. GeoEye-1 and WorldView-2 pan-sharpened imagery for object-based classification in urban environments , 2013 .