A high accuracy landcover classification method of multi spectral images using dempster- shafer model
Sun-Pyo Hong , Haruhisa Shimoda
Kiyonari Fukue, Toshibumi Sakata
Tokai University Research and Information Center
2-28-4 Tomigaya, Shibuya-ku, Tokyo 151, Japan
Abstract
Three new classification methods for multi spectral image are proposed. They are named as a like hood addition method a like hood majority method and a dumpster Shaffer rule method basic striates data and to combine obtained like hood for final classification these three methods use different combining algorithms.
From the classification experiments following results were obtained the method based on Demister Shafer's rule of combination showed about conventional method. This method needed about 16% more processing method showed 1% to 5% increase of classification accuracies how ever processing times of these of these two methods are almost the same with of a conventional method.
Introduction
With the launch of second-generation high resolution sensors like thematic mapper and HRV many kinds of researches have been done to certificate the capability of these sensors studies have shown that classification. Most of the results of these sensors have shown that classification accuracies using these sensors are not so high as expected when applying conventional supervised maximum like hood classifier using only spectral information. These results have made many researches to study spatial features like texture or more sophisticated classifier like expert systems or fuzzy classifiers.
One of promising methods, which can be through to increase classification accuracies, is to utilize multi spectral data. The most popular method of combining multi spectral temporal data is to just increase the dimensions of classification feature space. In other words multi temporal data are considered to be set of multi not necessarily shows improvements of classification accuracies because variances of each training data are usually increased this conventional method is called as a simple combination method in this image.
Proposed Methods
A pixel wise maximum like hood classifier based on spectral features is used as a basic classifier .Let be like hood of class c derived from multi temporal data set .in conventional simple combination (SC) method is calculated, L
c is calculated as
Where
c: Class, n : number of spectral bands m: number of temporal data.
t: transposed matrix determinant
S
c variance covariance matrix of class c
M
c mean vector of class -c x:pixel vector shown as
X = {x
1(t
1)x
2(t
1).............x
n,(t
1).x
1(t
2),x
2(t
3).........x
1(t
j),x
2(t
j),.................,x
n(t
j),.........,x
1(t
m),x
2(t
m),...........x
n(t
m)
t: ID of observation date,..........x
i:pixel value of spactral band
-1
Then a decision class is determined to the class showing the maximum like hood as follows
DC = C-max, if Lc-max=maxc[Lc]
In this method temporal features are treated as the same feature with spectral features .That is the dimension of feature space is equal to the product of the number of spectral bands and the number of nulti temporal usually increases decreases because the variance of each class usually increase compared to that of single temporal case. Consequently the SC method does not always show improvements of classification accuracies.
