a) The dataset 1, contain information from May 2010 to
Feb. 2011 (05 images). Dataset 1 has five time-series
images of pre-sowing (one image), flower to open ball
(three images) and harvesting (one image) stage
respectively.
b) The dataset 2, contain information from Nov. 10 to Feb.11
(04 images) flower to open ball (three images) and
harvesting (one image) stage.
c) Dataset 3 three time-series images were stacked from
Dec.10 to Feb.11 that contains information about flower
to open ball (two images) and harvesting (one image)
stage.
6. METHODOLOGY ADOPTED
The methodology adopted for this research work was broadly
divided into four stages as shown in figure 2.
Figure 2: Methodology adopted
In the present research work the AWIFS data sets are used.
While acquiring the raw time series multi-spectral data it has
been processed for atmospheric and geometric correction.
Training sites for cotton were identified on LISS-III and
AWIFS images with the help of global positioning system
(GPS) data and the visually interpreted FCC images. In order to
reduce the error the LISS-III images were geo-referenced using
Erdas AutoSynco, while AWIFS images were co-registered
with reference to LISS-III images. Output cell size for LISS-III
images was taken as 20 while for AWIFS 60 to have spatial
pixel size ratio of 1:3 between AWIFS and LISS-III images.
Common area of interest from all temporal images were
generated using the subset tool in Erdas imagine. To create the
models of the five band ratio vegetation index chosen, as
discussed in section indices and classification approaches,
ERDAS Model Maker was used.
As mentioned in Table 2 the different datasets were taken to
find out the most suitable time-series (multi-date) images.
Multi-date various vegetation indexes (NDVI, TNDVI, SR,
TVL and SAVI) from AWIFS and LISS-III scenes were
computed for the ground truth sites (Figure 3). The fuzzy set
theory based sub-pixel classification technique was used for
further classification. The samples of cotton were taken from
both AWIFS and LISS-III time-series images. For testing the
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
classification accuracy reference fraction images were used
from LISS-III sensor of IRS-P6 satellite having both the data
sets of same dates as of AWIFS data. The accuracy assessment
of sub-pixel classification output has been conducted using
fuzzy error matrix (FERM) (Binaghi ef al., 1999).
Different Indices of Cotton Crop from LISS-III Image
Indices Value
Months
Figure 3: Different indices of cotton crop from LISS-III image
7. RESULT AND DISCUSSION
In this paper, it has been presented how various indices along
with special form of noise classifier impact the accuracy of the
multi-temporal crop classification. For this the ALCM module
from, SMIC: Sub-Pixel Multi-Spectral Image Classifier
package (Kumar et al, 2006) has been used. The ALCM
module has capability to process multiple multi-spectral images
for single land cover class extraction at sub-pixel level using
supervised approach.
Table 4: Image to image overall accuracy of data sets for five
indices using noise classifier
Dataset SR NDVI Thy SAVI TVI
SET 1 93.40 65.74 91.44 90.93 86.89
SET 2 82.84 82.82 96.02 83.64 83.88
SET 3 80.84 81.40 95.96 84.27 91.58
The table 4 shows comparison of accuracy for indices NDVI,
SAVI, SR, TNDVI and TVI band ratio techniques when applied
on all the three data sets using NC. The best accuracy was from
second dataset of the TNDVI indices with 96.02% overall fuzzy
accuracy. The classified output using NC of three sets for five
vegetation indices are shown in Figure 4. It has been observed
that, if we take the images of pre-flowering, flowering maturity,
and harvesting stage, and for assessment take the same or
similar date’s images of testing and reference datasets, it will
give the best result for crop identification and discrimination.
CONCLUSION
The aim of this study was to map single crop of interest using
fuzzy based classifier with the help of time-series multi-spectral
satellite images. The crop under consideration in this work is
cotton cultivated in Aurangabad district of Maharashtra
province in India. Data used for this study was AWiFS (coarser
resolution) for soft classification and LISS-III (medium coarser)
data for soft testing from Resourcesat-1 (IRS-P6) satellite. The
output noise classifier (NC) along with the five indices has been
studied. NC classifier was evaluated in sub pixel classification
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