Integration of Magnitude and Shape Related Features in Hyperspectral Mixture Analysis to Monitor Weeds In Citrus Orchards.
ABSTRACT Traditionally, Spectral Mixture Analysis (SMA) fails to fully account for highly similar ground components or endmembers. The high similarity between weed and crop spectra therefore hampers the implementation of SMA for steering weed control management practices. To address this problem, the current study presents an alternative SMA technique, referred to as Integrated Spectral Mixture Analysis (iSMA). iSMA combines both magnitude (~reflectance) and shape (~derivatives) related features in an automated waveband selection protocol and allows for an optimal separation between weed and crops, irrespective of the scenario considered. Compared to traditional approaches iSMA significantly improved weed cover fraction estimations (~17% increase). Analysis was performed on different simulated mixed pixel spectra sets compiled from in situ measured weed, Citrus canopy and soil spectra.
[show abstract] [hide abstract]
ABSTRACT: In the effort of developing precision agriculture tools, remote sensing has been commonly considered as an effective technique for weed patch delineation, where weed infestations are detected based on variations in the plant canopy spectral response. Because the canopy spectral response is important for weed detection, discussions on the irradiative interaction of light in plant canopies and the effect of variable soil background on the canopy spectral response are presented in this review. Also, a presentation of the current techniques for removal of soil effects, including vegetation indices and spectral mixture analysis, shows that these techniques have not been adequately developed for use in remote sensing-based weed detection applications. Given the nature of light interaction in a plant canopy, this review proposes that the spectral response of a plant canopy depends on both the species and the biomass density. Remote detection of weeds from ground-, aircraft-, and satellite-based platforms has been accomplished on a wide scale, yet the use of these weed detection methods to make variable-rate herbicide applications has not occurred as often. By judging success based on variable-rate herbicide applications rather than precise weed localization, some of the current problems in weed sensing may be skirted.Precision Agriculture 09/2004; 5(5):477-508. · 1.55 Impact Factor
[show abstract] [hide abstract]
ABSTRACT: Linear spectral mixture analysis (SMA) has been used extensively in remote sen-sing studies to estimate the sub-pixel composition of spectral mixtures. The lack of ability to account for sufficient temporal and spatial variability between and among ground component or endmember spectra has been acknowledged as a major shortcoming of conventional SMA approaches. In an attempt to overcome this problem, a novel and automated linear spectral mixture protocol, referred to as stable zone unmixing (SZU), is presented and evaluated. Stable spectral features (i.e. least sensitive to spectral variability) are automatically selected for use in the mixture analysis based on a minimum InStability Index (ISI) criterion. ISI is defined as the ratio of the spectral variability within and the spectral variability among the endmember classes that are present within the mixture. The algorithm was tested on a set of scenarios, generated from in situ measured hyperspectral data. The scenarios covered both urban and natural environments under differing conditions. SZU provided reliable endmember cover distribution maps in all scenarios. On average, an absolute gain in R 2 —the coefficient of determination of the modelled versus the observed sub-pixel cover fractions—of 0.14 over the traditional SMA approaches was observed while the absolute gain in fraction abundance error was 0.06. It was concluded that the SZU protocol has potential to be an effective and efficient SMA algorithm for generating optimal cover fraction estimates regardless of the scenario considered. Moreover, the subset selection protocol, as implemented in SZU, can be regarded as complementary to conventional SMA approaches resulting in a further reduction of spectral variability.
[show abstract] [hide abstract]
ABSTRACT: The linear mixture model is frequently used to characterize surface cover over land, to model the reflectance of heterogeneous surfaces, and, by inversion, to estimate fractional cover from a multispectral satellite signal. It is usually assumed that certain parameters of this model, namely the so-called endmember spectra, are fixed, and that the model residual - the difference between a signal and its expected value in terms of the linear model - is systematically independent of all other parameters. In a small number of studies the endmember spectra have been allowed to have random fluctuations, giving rise to a covariance matrix for the residual that depends on the underlying proportions, and two distinct models exist for this mixed-pixel covariance matrix. In this note the linear model for mixed pixels is examined with varying endmember spectra, and it is shown that under a simple set of models for the variability of both endmembers and abundance, the covariance matrix for the residual is a weighted sum of the two previously considered cases. Generally, the balance between the two limiting cases is determined by the length scale for changes in the reflectance of any given cover type, and the length scale for changes in surface cover itself; one or other of the two limit models is preferred when these lengths are very different.IEEE Transactions on Geoscience and Remote Sensing 03/2006; · 2.89 Impact Factor
THE INTEGRATION OF MAGNITUDE AND SHAPE RELATED FEATURES IN HYPERSPECTRAL
MIXTURE ANALYSIS TO MONITOR WEEDS IN CITRUS ORCHARDS
B. Somers*1, K. Cools1, S. Delalieux1, W. W. Verstraeten1, J. Verbesselt2,S. Lhermitte1 & P. Coppin1
1 Dept. of Biosystems, M3-BIORES, Katholieke Universiteit Leuven,
Celestijnenlaan 200E, BE-3000 Leuven, Belgium
2CSIRO Forest Biosciences, Private Bag 10,
Clayton South, VIC 3169, Australia
* corresponding author; email: email@example.com
In an agricultural context hyperspectral mixture analysis (SMA) potentially bears the ability to map the spatial distribution of weeds and as
such allows for a site specific application of herbicides . The high similarity in spectral characteristics between weeds and crops,
however, leads to sub-optimal classification accuracies when conventional approaches are considered [2, 3]. The current study presents an
alternative SMA technique to address this problem. In an integrated approach original and derivative reflectance features are considered
simultaneously in a single analysis. As such, a perfect description of both the magnitude and shape of ground component spectra becomes
feasible. Subsequently, an automated waveband selection protocol tracks the spectral features that display the lowest spectral similarity.
The selected subset, which maximizes the inter-class variability while minimizing the intra-class variability, is incorporated in a mixture
analysis resulting in increased sub-pixel fraction estimate accuracy. The algorithm is validated both for simulated and in situ measured
mixtures of weed canopy, Citrus tree canopy and bare soil spectra.
Linear Spectral Mixture Analysis (SMA)
In linear spectral mixture analysis the observed spectrum r for any given pixel in the scene is expressed as:
M is a matrix of which each column corresponds to the spectral signal of a specific ground cover class (endmember) and f is a column
vector [f1,…,fm]T that denotes the sub-pixel cover fractions occupied by each of the m endmembers. The portion of the spectrum that cannot
be modeled is expressed as a residual term, ?. Sub-pixel endmember fractions for the corresponding vector f are obtained by minimizing
next equation considering the constraints of Eq. (1):
In a conventional SMA approach, image-wide endmember spectra are defined. The accuracy of sub-pixel fraction estimates therefore
decreases linearly with both the variability within and the similarity among endmember classes .
Derivative Spectral Mixture Analysis (DSMA)
Derivative endmember spectra are used to emphasize endmember specific diagnostic absorption features, while reducing differences in the
spectral magnitudes . The linear mixture model (1) is rewritten as:
is the nth derivative of the spectrum at wavelength ? of the mixed pixel,
is the nth derivative of the endmember matrix M.
Integrated Spectral Mixture Analysis (iSMA)
Original reflectance spectra (~ magnitude) and derivative spectra (~ shape) both provide independent and new information on the spectral
characteristics of ground components. We therefore propose to integrate both in one analysis. This integrated mixing model has the same
form as the reflectance/derivative mixing models of Eqs. (1) & (3) with the distinction that the least square regression estimator will rely on
both the equations of (1) and (3) to estimate f. A weighted least square regression approach is used to equalize the contribution of the
derivative and original reflectance features. This is necessary because
is relatively small compared to
which implies that the least square estimator is mainly determined by the original reflectance features if no weighting operation is
performed. Secondly, stable spectral features (i.e., least sensitive to spectral variability) are automatically selected for use in the mixture
analysis based on a minimum InStability Index (ISI) criterion . ISI is defined as the ratio of the spectral variability within (i.e., the sum
of the one-sided 95% confidence interval per endmember class) and the spectral variability among the endmember classes that are present
within the mixture (i.e., average Euclidean distance between the class means). The selected set of stable spectral features can be composed
of both original and/or derivative (1st and 2nd order) reflectance values.
The performance of iSMA to map the spatial distribution of weed patches in Citrus orchards was evaluated. Simulated [5, 6] and in situ
measured mixtures of weed canopy, tree canopy and bare soil spectra were analyzed. In situ measurements were performed using a full-
range (350-2500 nm) spectroradiometer (ASD, Boulder, CO). The abundance error (?f) and the coefficient of determination (R²) between
the estimated and real (i.e., ground truth) endmember fractions were used to assess classification accuracy .
Table 1: The performance of the SMA, DSMA and iSMA for different scenarios
R² Intercept Slope
mixtures of1: sim. in situ sim. in situ sim. in situ sim. in situ
Citrus sinensis & Lolium sp.
0.26 0.29 0.19 0.10 0.22 0.27 0.59 0.48
0.07 0.09 0.74 0.63 0.07 0.12 0.91 0.77
iSMA 0.06 0.08 0.87 0.75 0.06 0.09 0.91 0.86
Citrus sinensis, Echium sp.
0.14 0.11 0.51 0.32 0.07 0.23 0.80 0.61
& bare soil
0.05 0.09 0.88 0.67 0.00 0.05 0.97 0.80
Citrus retuculata, Plantago sp.
& bare soil
0.06 0.08 0.86 0.76 0.02 0.07 0.95 0.83
iSMA 0.04 0.06 0.88 0.79 0.02 0.05 0.95 0.86
1 A shade endmember is included in all mixtures; *Results are shown for the first derivatives only
DISCUSSION & CONCLUSIONS
The iSMA approach, which combines both shape and magnitude related spectral features in an automated waveband selection protocol,
allows for optimal weed patch monitoring in Citrus orchards. Both for the simulated and in situ measured mixtures iSMA performed better
than traditional mixture analysis approaches (Table 1). The sub-optimal accuracy achieved for in situ measured pixels is assigned to
nonlinear mixing caused by multiple photon scattering . Currently an attempt is made to improve the accuracy by modeling the
nonlinear interactions. As iSMA is insensitive to changing scenarios (Table 1), the algorithm provides a powerful tool as a dynamic weed
development monitoring technique. Moreover, the iSMA approach is relevant in any other ecosystem or application domain in which
highly similar endmembers need to be separated (e.g., urban environments ). Future research should, however, validate this statement.
 Thorp, K.R., & Thian, L.F. , “A review on remote sensing of weeds in agriculture”, Precision Agr., 5, pp. 477-508, 2004.
 Somers, B., Delalieux, S., Verstraeten, W.W., et al., “An automated waveband selection technique for optimized hyperspectral mixture
analysis”, Remote Sensing of Environment, under review.
 Settle, J., “On the effect of variable endmember spectra in the linear mixture model”, IEEE Trans. on Geosci. and Remote Sens., 44, pp.
 Zhang, J., Rivard, B., & Sanchez-Azofeifa, A., “Derivative spectral unmixing of hyperspectral data applied to mixtures of lichen and
rock”, IEEE Trans. on Geosci. and Remote Sens., 42, pp. 1934-1940, 2004.
 Asner, G.P., & Lobell, D.B., “A biogeophysical approach for automated SWIR unmixing of soils and vegetation”, Remote Sensing of
Environment, 74, pp. 99-112, 2000.
 Somers, B., Delalieux, S., Verstraeten, W.W., et al., “A conceptual framework for the simultaneous extraction of sub-pixel spatial
extent and spectral characteristics of crops”, Photogrammetric Eng. & Remote Sens., in press.
 Ray, T.W., & Murray, B.C., “Nonlinear spectral unmixing in Desert Vegetation”, Remote Sensing of Environment, 55, pp. 59-64, 1996.
 Song; C., “Spectral mixture analysis for subpixel vegetation fractions in the urban environment: how to incorporate endmember
variability?”, Remote Sensing of Environment, 95, pp. 248-263, 2005.