ar IX -v s(X,-v,) A(X,-v,)
V; = Mean vector for each class
2.4 Accuracy Assessment Approach
Silván-Cárdenas and Wang, (2007) developed theoretical
grounds, for a more general accuracy assessment of soft
classifications, which account for the soft class distribution
uncertainty.
In formal grounds, one requires the agreement-disagreement
measure to conform Eq. (16), where A and D denote agreement
: ; S F
and disagreement operators respectively, where '"* and ^"
denote the over and underestimation errors at pixel n in Eq (16).
A(s. ru) ifk=1 (16)
D(s iu) ifk zl
God
Sy = Sy, — Min (5,5)
n
Vr = Hu - min (s, 7)
Practically, it is convenient to express each confusion interval
in the form PB, £U,, where Py and Uy, are the interval center
and the interval half-width, respectively. These are computed as
indicated by Eq.(17) and (18), respectively. The general
structure of SCM is provided in Silvan-Cardenes and Wang
(2007). With the availability of IRS-P6 satellite data it is
possible to acquire spectrally same and spatial different data
sets of same area with same acquisition time. Due to the
uniqueness of availability of these data sets, soft fraction images
generated from coarser resolution data set (e.g. AWIFS, IRS-
P6) can be evaluated from fraction images generated from finer
resolution data sets (e.g. LISS-III, IRS-P6) as reference data set
acquired at same time.
For the uncertainty visualization and evaluation of the
classification results, the entropy criterion is proposed. This
measure AI SS by the following Eq.(17):
Entropy(x) = Y. (s log =[.(#)) (17)
For high uncertainty, the calculated entropy (Eq. (17)) is high
and inverse. Therefore this criterion can visualize the pure
uncertainty of the classification results.
3. STUDY AREA AND DATA USED
The study area for the present research work belongs to
Sitarganj Tehsil, Udham Singh Nagar District, Uttarakhand,
India. It is located in the southern part of the state. In terms of
geographic latitude/longitude, the area extends from
28?52'29"N to 28?54'20"N and 79?34'25"E to 79?36'34"E.
The area consists of agricultural farms with sugarcane and
paddy as one of the few major crops with two reservoirs
namely, Dhora and Bhagul reservoir. The images for this
research work have been taken from two different sensors
namely AWIFS and LISS-III belonging to satellite IRS-P6 as
shown in Figure 2.The AWIFS dataset used here for
classification and LISS III for referencing purposes.
4. METHODOLOGY
Two datasets (AWIFS, and LISS-III) were geometrically
corrected with RMSE less than 1/3 of a pixel and resampled
using nearest neighbour resample method at 60m, and 20m
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B3, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
spatial resolution respectively to maintain the correspondence
of a AWIFS pixel with specific number of LISS-III pixels (here
9 pixels of LISS-IIL, corresponding to one pixel of AWIFS)
with respect to sampling during accuracy assessment. The flow
chart of the methodology adopted is shown in Fig. 1. The six
classes of interest, namely deciduous forest, eucalyptus
plantation, water bodies, agriculture with crop, agriculture
without crop, and moist agriculture without crop have been
taken for this study work. Training data was collected with the
help of field data and testing was conducted while taking 100
samples per class and total 600 samples randomly selected.
In first part of this research work it has been tried to find out the
optimum value of weighting exponent *m' for FCM and PCM
classifiers after that performed the experimentation on noise
clustering without entropy based classifier where it has tried to
find out the optimum value of regularizing parameter (?) with
respect to fuzzy overall accuracy and fuzzy kappa coefficient.
The range of regularizing parameter has been taken from 1 to
40 with the interval of 10, and the values of weighting exponent
is varying from 1.4 to 3.2, fuzzy overall accuracy, fuzzy kappa
coefficient and uncertainty in accuracy parameters have been
estimated for different LISS-III and AWIFS data sets. It has
been observed that as regularizing parameter increases, fuzzy
overall accuracy as well as fuzzy kappa coefficient also
increases as shown in Fig. 3 and 5. But it has also observed that
uncertainty in fuzzy overall also increases in a given Fig. 4 and
6. So, it was important to decide what should be the appropriate
regularizing parameter value to be used in noise clustering
without entropy based fuzzy classifier.
. Coarse Resolution
| MXDaa
_ Pre-process |
Classification Expérimentes v
| a)FCMClassifier z T
Db)POMChsifier — —
€) Noise Clustering without Entropy :
Image to image accuracy
Assessment
LISS-III
Fig. 2: Location of study area
TI
rel
clé
th
