Extraction and Utilization of Geometrical and Contextual Information in very High Resolution IKONOS Satellite Imagery
We have tested the performance of this technique with several scenes of the pan-sharpened 3-2-1 bands of the IKONOS images. Though the images are in RGB colours, at this stage we only use the red band. The intermediate result of one of them is presented in Figure 2. The original scene image is 400 pixels by 400 pixels in size. Figure 2 covers a zoomed-in area. The performance of this technique is verified with human eyes, which spotted 1803 trees in this region. With this technique, 1855 trees are counted by the automatic procedure. The accuracy of the technique is evaluated by the equation:
Accuracy = 1 - |no. of trees automatically counted - actual no. of trees| / actual no. of trees
Using this equation, the accuracy of automatic counting is 97.2%. Of the 1855 trees detected by the algorithm, 1535 are located correctly at the crown centers, 320 are mistaken as trees, and 268 trees are missed. We found that the errors are mainly due to the mis-location of the tree crowns. Instead of locating the trees at the centre of the tree crowns (which can be detected visually) the detected tree locations are displaced away from the crown centres. Hence, an error in mis-location contributes to both the mistaken tree and missing tree errors when validated by a human observer. There are four other scenes tested with this technique. There results are tabulated in Table 1.
Table 1 Performance of the tree counting technique
| Scene
|
Number of trees counted visually
|
Number of trees counted automatically
|
Counting accuracy (%)
|
Correctly spotted by the technique
|
Wrongly spotted by the technique
|
Missed by the technique
|
| 1
|
1012
|
1018
|
99.4
|
928
|
90
|
84
|
| 2
|
1823
|
1697
|
93.1
|
1410
|
287
|
413
|
| 3
|
1733
|
1765
|
98.2
|
1565
|
200
|
168
|
| 4
|
1710
|
1756
|
97.3
|
1490
|
266
|
220
|
3. Road Extraction in Urban Areas
Another potential application of the IKONOS image is to extract road-network information of any areas. Roads appear as linear curves at low resolution, but the resolution of the IKONOS image is fine enough for us to observe the details of the road condition and structures that are along the roadside or are on the roads. The presence of these details actually destroys the homogeneity of the roads' geometrical features and thus makes the extraction of roads difficult using techniques such as ridges, semivariograms, and local orientation. Colour images generally offer an advantage over greyscale ones, though there is no standard colour for roads.
We are investigating a semi-automatic method that is based on colour and active contour. First a supervised colour-based region classification is used to extract out regions of the "road colour". Then, active contour is used on the outcome of the classification to extract curves and lines from the regions of the "road colour". The usual active contour method uses intensity change as the internal energy. Instead of intensity change, we use the homogeneity of the region. In this paper, we discuss first the supervised classification. The active contour part is under development.
Figure 3: Intermediate results of road extraction
We worked on the 4-3-2 bands of an IKONOS image of a city region. First, the NDVI value is used to determine potential vegetation areas. Regions with positive NDVI values are all put into the class called vegetation. Then, 5 other classes are defined. They are: dull roofs, bright roofs, buildings, dark smooth areas (including shadows and waters), and road. Spectral signatures for these classes are obtained from training sites in the image by the human supervisor. The mean and covariance matix are computed for each class. Subsequently, all the pixels in the image are classified to the class of closest colour using the Mahalanobis distance. Figure 3(b) shows the
