that wavelet transform performed better than the fourier
transform. Kumar and Roy, 2010, has worked with add on
bands in multi-spectral dataset of Worldview -2. This work has
proposed class based sensor independent spectral band ratio
NDVI approach for extracting crop information. Yang et al.,
2008, has identified that the accuracy of surface feature
recognition is improved greatly, by introducing fuzzy statistics
variables into classical principal component analysis (PCA)
methods on applying to the multi-spectral Landsat ETM- data
for image enhancement. Kumar and Saggar, 2008, have found
that possibilstic fuzzy classifier can be used for single class
extraction of interest. Class based ratio data was used as input
in possibilistic fuzzy classifier and water class has been
identified at sub-pixel level. It was also observed from this
approach that shadow pixels were not mixing with water class
pixels. Acharyya et al., 2003, has studied a feature extraction
method based on m-band wavelet packet frames for segmenting
remotely sensed images. These wavelet features are then
evaluated and selected using an efficient neuro fuzzy algorithm.
The effectiveness of the methodology was demonstrated on two
four-band Indian Remote Sensing satellite (IRS-1A) images
containing five to six overlapping classes and a three-band
SPOT image containing seven overlapping classes. Dave 1991,
has introduced the concept of characterization and detection of
noise in clustering. He has presented the approach which is
applicable to a variety of fuzzy clustering algorithms as well as
regression analysis. Dave and Krishnapuram 1997, has studied
that the classical approach to clustering based on variations of
the K-means or the fuzzy c-means is not robust. The alternative
formulations based on noise clustering or possibilistic
clustering is robust in that they can be shown to be founded on
robust statistics. Banerjee and Davé 2005, proposed a scheme,
called as mega-clustering algorithm is shown to be robust
against outliers. Another interesting property is its ability to
distinguish between true outliers and non-outliers (vectors that
are neither part of any particular cluster nor can be considered
true noise). Robustness is achieved by scaling down the fuzzy
memberships, as generated by FCM. A lot of work has been
done in the field of single class extraction through time series
multi-spectral data but while going through the literature it has
been identified that the effects of various band ratio indices for
fuzzy noise classifier along with crop phenology has not been
explored in the past.
2. INDICES AND CLASSIFICATION APPROACHES
To enhance the vegetation signal in remotely sensed data and
provide an approximate measure of green vegetation amount, a
number of spectral vegetation indices have been proposed. By
combining data from multiple bands into single values, because
they correlate the biophysical characteristics of the vegetation
of the land cover from the satellite spectral signals.
A common practice in the remote sensing is the use of band
ratio to eliminate the various albedo effects. Jordan (1969), first
presented the ratio vegetation index (RVI) or simple ratio (SR).
Rouse et al, 1973, further suggested the most widely used
normalized difference vegetation index (NDVI) to improve
identification of vegetated areas and their conditions. However,
the NDVI index is saturated in high biomass and it is sensitive
to a number of perturbing factors, such as atmospheric effects,
cloud, soil effects, and anisotropic effects, etc. Therefore, a
number of derivatives and alternatives to NDVI have been
proposed in the scientific literature to address these limitations.
Tucker (1979), presented a transformed normalized difference
vegetation index (TNDVI) by adding a constant 0.5 to NDVI
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
and taking the square root. It always has positive values and the
variances of the ratio are proportional to mean values. TNDVI
indicates a slight better correlation between the amount of green
biomass and that is found in a pixel (Senseman ef al. 1996). To
reduce the impact to the NDVI from the soil variations in lower
vegetation cover areas, Huete (1988) proposed a soil-adjusted
vegetation index (SAVI) by introducing a correction factor L
(Zhengwei et al., 2008). Broge and Leblanc (2000), developed
triangular vegetation index (TVI), which describes the radiative
energy absorbed by the pigments as a function of the relative
difference between red and near-infrared reflectance in
conjunction with the magnitude of reflectance in the green
region, where the light absorption by chlorophyll a and b is
relatively insignificant. Table 1 show different indices studied
in this work.
Vegetation Index Equation References
Birth and
Pp. McVey,
Simple Ration (SR) SR = EE 1968;
td Colombo et
al.,2003
Rouse et
Normalized al., 1973;
Difference NDVI = Fair — Pred Deering et
Vegetation Index Pair + Fred al. 1975;
(NDVI) Huete et al.,
2002
; ; Huete and
Sal Se 1 + LY Bir Fred) Liu, 1994;
Vegetation Index SAVI pr Rania B
(SAVI) nir * ^ red al mm
Triengular TVI =0.5(120(P,ir — Pereen)) | Broge and
Vegetation Index - 200(P ed 7 Preen Ter.
(TVI)
Transformed Tucker,
Normalized 1 1979
Difference 2 nir — Pred }- 0.5 ?
Vegetation Index Bair + Pred
(TNDVI)
Table 1: Various indices studied in this work
3. NOISE CLASSIFIER
The concept of "Noise Cluster' is introduced such that noisy
data points may be assigned to the noise class. The approach is
developed for objective functional type (K-means or fuzzy K-
means) algorithms, and its ability to detect 'good' clusters
amongst noisy data is demonstrated. Clustering methods need to
be robust if they are to be useful in practice. Uncertainty is
imposed simultaneously with multispectral data acquisition in
remote sensing. It grows and propagates in processing,
transmitting and classification processes. This uncertainty
affects the extracted information quality. Usually, the
classification performance is evaluated by criteria such as the
accuracy and reliability. These criteria cannot show the exact
quality and certainty of the classification results. Unlike the
correctness, no special criterion has been put forth for
evaluation of the certainty and uncertainty of classification
results. It follows the uncertainty problem in multispectral data
classification process. Several uncertainty criteria are
introduced and applied in order to evaluate the classification
performance as membership value generation have been shown
in equation (1) and (2).
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