The development of interactive decision boundary determination method in the feature space of remotely sensed data
Minoru Akiyama, Yasushi Shimoyama
Tamio Sekiguchi ,Takashi Hayashi, Takio Mizuno
Photogrammetric research and development office
geographicalsurvey institute Kitazato-1
Taukuba City Ibaraki Pref. Japan
Abstract
In ordinary supervised classification methods a decision boundary is automatically determined based on the statistical likelihood of each classes whose probability function was calculated from training samples therefore when attempting to improve classification accuracy we can do nothing but recollection of training samples however not always but recollection of training samples how ever not always effective despite that it is quite time and cost consuming .
On the other hand the displayed histogram of data gives us the information how the data are distributed in the feature space visually and may enable us to draw a decision boundary by referring the displayed data clusters more ever if an analyst has some knowledge for the land use pattern of the study area he could adjust the decision boundary by referring the distribution of training samples classified result from the initial decision boundary and so on
In this study a new method has been developed to determine a decision boundary directly on the feature space without the help of training sample feature this method consists of the following sub processes.
- Data compression by principal component
transformation.
- Displaying the two dimensional data histogram
of the first two principal component data in gray level.
- Collection of training samples and displaying
their distribution over the data histogram data when necessary.
- Manual determination of a decision boundary on
the principal component plane referring to the data histogram and the
distribution of the training samples.
- Classification of image data by a looking up
the table created by the decision boundary mentioned above.
- Evaluation of a classified result.
- Repetition of the process from 3 through 6 if necessary.
The characteristic of this method is that we can take human intelligence experience and knowledge in account setting the decision boundary in the feature space. This method enables us not only to reduce the time and cost for classification but to land the classified result to the direction as we like In addition through the case study it became assured that this method has enough accuracy and efficiency for practical use.
Methodology
The flow of this method original multi channel image data compressed so that two dimensional image data by using principal component analysis so that its histogram can be expressed on a plane in order to make each component axis are interactively plane scale and range of each determined by referring the minimum and maximum values of the first. Two principal .Training samples are colleted if necessary on the color composite image of the original channel data. then the distribution of the training samples is displayed over the histogram which may help a person to understand which cluster is corresponding to which class by referring the two dimensional histogram and training sample distribution decision samples distribution decision boundary is determining decision is interactively if the histogram is not well separated in to desired classes rough segmentation in to a few combined classes is executed .Fine segmentation is then executed by using the third or fourth principal component plane recalculated from the data involved in the decision class.
Since each segment divided by the determined decision boundary represents the area in the feature plane corresponding to each class classification by pixel is done by table look up method while the look up table is aforementioned class divided principal component plane.
Classified result is visually inspected and evaluated if there is any pixel obviously wrong it might be corrected by resetting the decision boundary.
Fig.1 Flow of the interactive Decision Boundary Determination Method
The final classification is made after several trial and error is decision boundary editing.
