IX-B8, 2012
ges were used
> both the data
acy assessment
onducted using
).
"Ill Image
ISS-III image
s indices along
iccuracy of the
ALCM module
age Classifier
| The ALCM
spectral images
xel level using
a sets for five
À VI TVI
).93 86.89
.64 83.88
1.27 91.58
indices NDVI,
s when applied
ıracy was from
o overall fuzzy
ce sets for five
been observed
ering maturity,
> the same or
atasets, it will
crimination.
[interest using
multi-spectral
n this work is
' Maharashtra
WiFS (coarser
dium coarser)
) satellite. The
dices has been
| classification
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
approach and fuzzy accuracy assessment has been carried out
using FERM. It is found that the maximum accuracy achieved
was 96.02% for TNDVI index of dataset 2. According to
results obtained from this work, selection of suitable temporal
data sets, appropriate band ratio and use of fuzzy based
classifiers, helps in handling mixed pixels in coarser data sets.
SET 2
NDVI
SR
TNDVI
TVI
us Membership Value
Figure 4:Output showing cotton crop using Noise Classifier
REFERENCES
Acharyya M, De RK and Kundu M, (2003), Segmentation of
Remotely Sensed Images Using Wavelet Features and Their
Evaluation in Soft Computing Framework, IEEE Transactions
On Geoscience And Remote Sensing, Vol. 41, No. 12, pp.
2900-2905.
Banerjee A. and Davé R. N., (2005), The Fuzzy Mega-cluster:
Robustifying FCM by Scaling Down Memberships ©
Springer-Verlag Berlin Heidelberg, LNAI 3613, pp. 444 — 453.
Binaghi E., Brivio P. A., Chessi P. and Rampini A. (1999), A
fuzzy Set based Accuracy Assessment of Soft Classification.
Pattern Recognition letters 20: pp. 935-948.
Birth, G. S. and McVey G., (1968) Measuring the color of
growing turf with a reflectance spectroradiometer, Agronomy
Journal, 60:640-643.
Broge N. H. and E. Leblanc (2000). Comparing Predicting
Power and Stability of Broadband and Hyperspectral
vegetation Indicies for Estimation of green area index and
canopy Chlorophyl density, Remote Sensing Environment, 76
(2000): 156-172.
Colombo R., Bellingeri D., Fasolini D. and Marino C. M.
(2003) Retrieval of leaf area index in different vegetation types
using high resolution satellite data. Remote Sensing of
Environment, 86, 120-131.
Dave R. N., (1991). Characterization and detection of noise in
clustering Pattern Recognition Letters 12 , pp. 657-664.
Dave R. N and Krishnapuram R, 1997. Robust Clustering
Methods: A Unified View. IEEE Transactions on Fuzzy
Systems, Vol. 5, No. 2, pp. 270-293.
Deering D. W., Rouse J. W., Haas R. H. and Schell J. A. (1975)
Measuring “Forage Production” of grazing units from Landsat
MSS data, Proceedings of the 10^ International Symposium on
Remote Sensing of Environment, II:1169-1178.
Huete A. R. (1988). A soil-adjusted vegetation index (SAVI),
Remote Sensing of Environ, Volume 25, Issue 3, August 1988,
pp. 295-309.
Huete A. and Liu H. (1994) An error and sensitivity analysis of
the atmospheric-correcting and soil-correcting variants of the
NDVI for the MODIS-EOS. IEEE Transactions on Geoscience
and Remote Sensing, 32(4), 897-905.
Huete A., Didan K., Miura T., Rodriguez E. P., Gao X. and
Ferreira L. G. (2002) Overview of the radiometric and
biophysical performance of the MODIS vegetation indices,
Remote Sensing of Environment, 80(1-2): 195-213.
Jordan, C.F.,(1969).. Derivation of leaf-area index from quality
of light on the forest floor, Ecology, 50, pp. 663—666.
Kumar A. and Roy P. S., (2010). Effects on specific crop
mapping using worldview-2 Multispectral add on bands-A soft
classification approach, Geospatial World Forum, Hyderabad,
India.
Kumar A. and Saggar S., (2008). Class Based Ratioing Effect
On Sub-Pixel Single Land Cover Automatic Mapping,
International Archives Of The Photogrammetry, Remote
Sensing And Spatial Information Sciences, VXXXVII, Part B7,
pp. 527-532.
Kumar A., Ghosh S. K. and Dadhwal V.K., (2006). Sub-Pixel
Land Cover Mapping: SMIC System. ISPRS International
Symposium on “Geospatial Databases for Sustainable
Development” Goa, India, September 27-30.
Linlin L. and Huadong G., (2008). Wheat Phenology Extraction
from Time-Series of. SPOT/VEGETATION data, Congress on
image and signal processing, Beijing, China, pp. 794-797.
Lucas R., Rowlands A., Brown A., Keyworth S. and Bunting P.,
(2007). “Rule-based classification of multi-temporal satellite
imagery for habitat and agricultural land cover mapping",
ISPRS Journal of photogrammetry and Remote Sensing, Vol.
62, Issue 3, pp.165-185.
Tingting L. and Chuang L., (2010). Study on extraction of crop
information using time-series MODIS data in the Chao Phraya
Basin of Thailand, Advances in Space Research, Volume 45(6),
pp. 775-784.
