Magnitude- and Shape-Related Feature Integration in Hyperspectral Mixture Analysis to Monitor Weeds in Citrus Orchards

Traditionally, spectral mixture analysis (SMA) fails to fully account for highly similar ground components or endmembers. The high similarity between weed and crop spectra hampers the implementation of SMA for steering weed control management practices. To address this problem, this paper presents an alternative SMA technique, referred to as Integrated Spectral Unmixing (InSU). InSU combines both magnitude (i.e., reflectance) and shape (i.e., derivative reflectance) related features in an automated waveband selection protocol. Analysis was performed on different simulated mixed pixel spectra sets compiled from in situ-measured weed canopy, Citrus canopy, and soil spectra. Compared to traditional linear SMA, InSU significantly improved weed cover fraction estimations. An average decrease in fraction abundance error (Deltaf) of 0.09 was demonstrated for a signal-to-noise ratio (SNR) of 500 : 1, while for a SNR of 50 : 1, the decrease was 0.06.

[1]  Francisca López-Granados,et al.  Mapping Ridolfia segetum patches in sunflower crop using remote sensing , 2007 .

[2]  Hairong Qi,et al.  Endmember Extraction From Highly Mixed Data Using Minimum Volume Constrained Nonnegative Matrix Factorization , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Alfred Stein,et al.  Abundance Estimation of Spectrally Similar Minerals by Using Derivative Spectra in Simulated Annealing , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Jarno Mielikäinen,et al.  Lossless Compression of Hyperspectral Images Using a Quantized Index to Lookup Tables , 2008, IEEE Geoscience and Remote Sensing Letters.

[5]  Gregory Asner,et al.  Endmember bundles: a new approach to incorporating endmember variability into spectral mixture analysis , 2000, IEEE Trans. Geosci. Remote. Sens..

[6]  W. Verstraeten,et al.  A Conceptual Framework for the Simultaneous Extraction of Sub-pixel Spatial Extent and Spectral Characteristics of Crops , 2009 .

[7]  Derek M. Rogge,et al.  Iterative Spectral Unmixing for Optimizing Per-Pixel Endmember Sets , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Changshan Wu,et al.  Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery , 2004 .

[9]  Jessica A. Faust,et al.  Aviris Radiometric Laboratory Calibration, Inflight Validation and a Focused Sensitivity Analysis in 1998 , 2000 .

[10]  Andrew Hall,et al.  Discrimination of blackberry (Rubus fruticosus sp. agg.) using hyperspectral imagery in Kosciuszko National Park,NSW, Australia , 2007 .

[11]  A. Savitzky,et al.  Smoothing and Differentiation of Data by Simplified Least Squares Procedures. , 1964 .

[12]  Antonio J. Plaza,et al.  Impact of Initialization on Design of Endmember Extraction Algorithms , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Ruiliang Pu,et al.  Extraction of red edge optical parameters from Hyperion data for estimation of forest leaf area index , 2003, IEEE Trans. Geosci. Remote. Sens..

[14]  S. Gerstl,et al.  Nonlinear spectral mixing models for vegetative and soil surfaces , 1994 .

[15]  Antonio J. Plaza,et al.  A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[16]  Jing Wang,et al.  Applications of Independent Component Analysis in Endmember Extraction and Abundance Quantification for Hyperspectral Imagery , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Ye Zhang,et al.  Integration of Spatial–Spectral Information for Resolution Enhancement in Hyperspectral Images , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[18]  H. Poilvé,et al.  Hyperspectral Imaging and Stress Mapping in Agriculture , 1998 .

[19]  M. S. Moran,et al.  Opportunities and limitations for image-based remote sensing in precision crop management , 1997 .

[20]  Ruiliang Pu,et al.  Estimation of yellow starthistle abundance through CASI-2 hyperspectral imagery using linear spectral mixture models , 2006 .

[21]  S. M. Jong,et al.  Improving the results of spectral unmixing of Landsat thematic mapper imagery by enhancing the orthogonality of end-members , 2000 .

[22]  Harald van der Werff,et al.  Assessing the Influence of Reference Spectra on Synthetic SAM Classification Results , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[23]  S. Delalieux,et al.  An automated waveband selection technique for optimized hyperspectral mixture analysis , 2010 .

[24]  Alessandro Mecocci,et al.  Theoretical and experimental assessment of noise effects on least-squares spectral unmixing of hyperspectral images , 2005 .

[25]  D. Lobell,et al.  A Biogeophysical Approach for Automated SWIR Unmixing of Soils and Vegetation , 2000 .

[26]  D. Roberts,et al.  Practical limits on hyperspectral vegetation discrimination in arid and semiarid environments , 2001 .

[27]  John B. Adams,et al.  Classification of multispectral images based on fractions of endmembers: Application to land-cover change in the Brazilian Amazon , 1995 .

[28]  Richard L. Church,et al.  Mapping Chaparral in the Santa Monica Mountains Using Multiple Spectral Mixture Models , 1996 .

[29]  Amy E. Parker Williams,et al.  Estimation of leafy spurge cover from hyperspectral imagery using mixture tuned matched filtering , 2002 .

[30]  Benoit Rivard,et al.  Intra- and inter-class spectral variability of tropical tree species at La Selva, Costa Rica: Implications for species identification using HYDICE imagery , 2006 .

[31]  J. C. Taylor,et al.  Sensitivity of mixture modelling to end‐member selection , 2003 .

[32]  Benoit Rivard,et al.  Derivative spectral unmixing of hyperspectral data applied to mixtures of lichen and rock , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[33]  Ben Somers,et al.  A weighted linear spectral mixture analysis approach to address endmember variability in agricultural production systems , 2009 .

[34]  S. Tompkins,et al.  Optimization of endmembers for spectral mixture analysis , 1997 .

[35]  John R. Miller,et al.  Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data , 2001, IEEE Trans. Geosci. Remote. Sens..

[36]  Jeff Settle,et al.  On the effect of variable endmember spectra in the linear mixture model , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[37]  David Lamb,et al.  PA—Precision Agriculture: Remote-Sensing and Mapping of Weeds in Crops , 2001 .

[38]  D. Peddle,et al.  Spectral mixture analysis of agricultural crops: Endmember validation and biophysical estimation in potato plots , 2005 .

[39]  Glenn J. Fitzgerald,et al.  Multiple shadow fractions in spectral mixture analysis of a cotton canopy , 2005 .

[40]  Jiang Li,et al.  Correction to "Wavelet-Based Feature Extraction for Improved Endmember Abundance Estimation in Linear Unmixing of Hyperspectral Signals" , 2004 .

[41]  Richard G. Oderwald,et al.  Spectral Separability among Six Southern Tree Species , 2000 .

[42]  Conghe Song,et al.  Spectral mixture analysis for subpixel vegetation fractions in the urban environment: How to incorporate endmember variability? , 2005 .

[43]  W. Verstraeten,et al.  Nonlinear Hyperspectral Mixture Analysis for tree cover estimates in orchards , 2009 .

[44]  Shiv O. Prasher,et al.  HYPERSPECTRAL IMAGE CLASSIFICATION TO DETECT WEED INFESTATIONS AND NITROGEN STATUS IN CORN , 2003 .

[45]  L. Tian,et al.  A Review on Remote Sensing of Weeds in Agriculture , 2004, Precision Agriculture.