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
TEMPORAL INDICES DATA FOR SPECIFIC CROP DISCRIMINATION USING FUZZY
BASED NOISE CLASSIFIER
Vijaya Musande*', Anil Kumar" , Karbhari Kale^ and P. S. Roy”
? Jawaharlal Nehru Engineering College, Aurangabad
^ Indian Institute of Remote Sensing, Dehradun
* Dept. of CS & IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad
KEYWORDS: Indices, Fuzzy Error Matrix (FERM), Noise Classifier (NC).
ABSTRACT:
Evaluation of fuzzy based classifier to identify and map a specific crop using multi-spectral and time series data spanning over one
growing season. The temporal data is pre-processed with respect to geo-registration and five spectral indices SR (Simple Ratio),
NDVI (Normalized Difference Vegetation index), TNDVI (Transformed Normalized Difference Vegetation Index), SAVI (Soil-
Adjusted Vegetation Index) and TVI (Triangular Vegetation Index). The noise classifier (NC) is evaluated in sub pixel classification
approach and accuracy assessment has been carried out using fuzzy error matrix (FERM). The classification results with respect to
the additional indices were compared in terms of image to image maximum classification accuracy. The overall accuracy observed in
dataset 2 was 96.03% for TNDVI indices, using NC. Data used for this study was AWIFS for soft classification and LISS-III data
for soft testing generated from Resourcesat-1(IRS-P6) satellite. The research indicates that appropriately used indices can
incorporate temporal variations while extracting specific crop of interest with soft computing techniques for images having coarser
spatial and temporal resolution remote sensing data.
1. INTRODUCTION
Time series of acquired multispectral image
represent characteristics of a landscape and each element
represented has a particular spectral response, which allows the
researcher to get highly relevant information to make
decisions without going to the field. Since objects including
vegetation, have their unique spectral features (reflectance or
emission response), they can be identified from remote sensing
imagery according to their unique spatial characteristics. The
strong contrast of absorption and scattering of the red and near
infrared bands can be combined into different quantitative
indices of vegetation conditions. The time series of such
vegetation indices observed over a period can help in further
classification of the vegetation as crop and other type of
vegetation. Classification techniques for grouping cluster and
finding substructure in data needs to be robust. By robustness
we mean that the performance of an algorithm should not be
affected significantly by small deviations from the assumed
model and it should not deteriorate drastically due to noise and
outliers. Robust statistics can be related to the concept of
membership functions in fuzzy set theory or possibility
distributions in possibility theory. This might explain the claim
made by the proponents of fuzzy set theory that a fuzzy
approach is more tolerant to variations and noise in the input
data when compared with a crisp approach.
The immensely popular k-Means is a partitioning procedure
that partitions data based on the minimization of a least squares
type. The fuzzy derivative of k-Means known as Fuzzy c-
Means (FCM) is based on a least squares functional; it is
susceptible to outliers in the data. The performance of FCM is
known to degrade drastically when the data set is noisy. This is
similar to least square (LS) regression where the presence of a
single outlier is enough to throw off the regression estimates.
The need has therefore been to develop robust clustering
algorithms within the framework of fuzzy c-means (FCM)
(primarily because of FCM's simplistic iterative scheme and
good convergence properties). The usual FCM minimization
constraints are relaxed to make the resulting algorithm robust.
The possibilistic c-means (PCM) algorithm was developed to
provide information on the relationship between vectors within
a cluster. Instead of the usual probabilistic memberships as
calculated by FCM, PCM provides an index that quantifies the
uniqueness of a data vector as belonging to a cluster. This is
also shown to impart a robust property to the procedure in the
sense that noise points are less unique in good clusters. Another
effective clustering technique based on FCM is the noise
classifier (NC) algorithm which uses a conceptual class called
the noise classifier to group together outliers in the data. AII
data vectors are assumed to be a constant distance, called the
noise distance, away from the noise cluster. The presence of the
noise cluster allows outliers to have arbitrarily small
memberships in good clusters (Banerjee and Davé 2005).
Till date many researchers in remote sensing field have applied
time series indices to study cropping pattern. Tingting and
Chuang, 2010, used the time-series NDVI to identify common
vegetation types or cropping patterns. They applied principal
component analysis, linear spectral un-mixing method and
support vector machine to classify cropland. Panda et al., 2010,
has studied four widely used spectral indices to investigate corn
crop yield. Back Propagation Neural Network (BPNN) model
was developed to test the efficiency of four vegetation indices
in corn crop yield production. Yang et al. 2009, has given a new
vegetation index which is robust to low vegetation and sensitive
to high vegetation and has potential to be an alternative to
NDVI for crop condition monitoring. Te-Ming et al., 2009, has
proposed new vegetation index by integrating with a Fast
Intensity-Hue-Saturation (FIHS) for high resolution imagery
which can extract and enhance green vegetation an alternative
to NDVI. Wardlow and Egbert, 2008, used a hierarchical crop
mapping to classify multi-temporal NDVI data. Linlin and
Huadong, 2008, presented a simple phenology model to identify
wheat crop using curve fitting procedure. Lucas et al., 2007,
studied the use of time-series of Landsat sensor data using
decision rules based on fuzzy logic to discriminate vegetation
type. They found that the rule-based classification gave a good
representation of the distribution of habitats and agricultural
land. Sakamoto et al, 2005, developed a new method for
remotely determining phenological stages of paddy rice. As for
the filtering, they adopted wavelet and Fourier transforms.
Three types of mother wavelet (Daubechies, Symlet and
Coiflet) were used. As the result of validation, it was observed
