A high accuracy landcover classification method of multi spectral images using dempster- shafer model
Three new methods of utilizing multi temporal data have been tested in this research .the first and second on are named like hood addition method and like hood majority method respectively the last one is based on Dempster sharfer rule of combination and is named as a DR method in these proposed methods the like hood of each class is calculated from each temporal image that is the like hood of class obtained from temporal image t is calculated for a pixel vector x ={x
1(t),x
2(t),........x
n(t)} last the like hood claculated from temporal data.
- like hood adding method
A score of class-c, S(c), is calculated in the LA method by the following equation.
where. k is the number of classes.
A decision class(DC) is determined to the class "c- max" if S(c-max) shows the mazimum score. which can be written as follows;
- Like hood majority (LM) method
In the LM method scoring of like hood and decision of class are calculated using eq. (4) and (5) respectively
note that the function f coverts the value of Lc(t) to binary data.
- Dempster rule DR method
Dempster rule of combination is expressed by the following formula/1
for a¹f, where K is the normalization coefficient and are expressed as
Eq.(6) Shows that the degree of evidence from the first source which focuses on set B and the degree of evidence mc t2 from the second source which focuses on set c are combined by taking the product mb t1 mc t2 which focuses on the inter section of B and C this is exactly the same way in which the joint probability distribution is calculated from two independent marginal distributions.
In this research is treated as a score of each classes B and C are defined as a subset of 1st 2nd and 3rd candidates of decision classes for each temporal data in order to aboid computational explosion 1st 2nd and 3rd candidates of decision classes correspond to classes having the largest 2nd largest and 3rd and when it is assumed that C1 C2 are the 1st 2nd and 3rd candidate class respectively the sub set B and C are expressed by.
B= {C1,C2,C3,C1 U C3 C2 U C3 c1 U C3) (7)
C={C1,C2,C3,C1 U C3 C2 U C3 c1 U C3) (8)
Then mB (t1) and mc t2 are calculated by the formula.
And a decision class is determined as follows.
If j of "tj" is greater than 2 such as t = {t1,t2,t3,t4,..........}, S(c) is calculated for all tj by applying eq. (10) iteratively.
Experiments
In order to evaluate purposed methods described in chapter following four seasonal land sat TM data were classified by using new three methods anda conventional SC method.
[Object area}
Sagami river basin ( in Japan) which has area of 12.8 km X12.0km
[Observation area]
Nov 4 91984) jan 23 (1985) aug 6 (1986) and may 21 (1987)
{image size}
512x480 pixels size =25m x 25m
[Used channels}
TM Ch 1,2,3,4,5, and 7
figure 1 shows test images used in the experiments these images were registered in the identical UTM coordinate system.
Items in left hand side in table 1 shows 15 classification categories used in the experiments howevrt the total number of classification classes were fifty nine since each the same training area for each seasonal image thus training data set are consisted of four Training data cores spending to four seasonal images.
At the first stage like hoods of each class ie were calculations in all test images by using each training data. this calculation done independently for each test image that is Lc (t1) Lc (t2} is calculated by using training data corresponding to the test image of t1 t2 t3 respectively.
At the second stage three proposed methods ( the LA LM and DR methods ) were applied to four seasonal like hood data obtained in the conventual's methods land cover classification using the first stage on the other hand performed according to eq 1 and 2 in order to compare with a case of single temporal classification conventional maximum like hood of classification wee conducted for each set images the same training data set 2 shows classification results.
Processing times of MLC for a single image was about 15 minutes those of the SC LA and LM methods are about 60 minutes because of process for foue seasonal images. the DR methods needed processing times or about 70 minutes experiments were done by using HP9000 /835 mini computer system
Finally classification accuracies were estimated quantitatively by using digital test site data as shown in fig 3 test site data contain about 50 land cover use categories .As the categories used in test site data differ from these in land cover classification conducted in this experiments fifteen classification categories were merged to five major categories were merged to five major categories as shown in table 1 accuracy evaluations were performed based on these five major categories.
The MLC for each test image shows average accuracies from 62% to 65% the SC method indicates an average accuracy of 65% which is almost the same value to the largest none of the MLC result LA LM and DR method showed about 1% 5% and 12% larger average accuracies respectively combined to the results of the MLC and the SC method.
