3.1. Image Acquisition
Healthy green grass has a strong reflection in the near infrared region. Therefore, they appear bright red on color infrared images. However, varnished grass often shows low reflection rate in the near infrared area, thus it appears dark red on the color infrared image. According to this, change of the environment has been simulated. A normal color film has been put in one 50mm single lens camera, and a color infrared film has been put in another 50 mm single lens camera. Control points have also been set up around the experimental area. To observe the reflection difference on both healthy and false green plants, four small areas are varnished in green. A pixel size of 400dpi was used for scanning. Four digital color images were generated in the size of 450 x 210, and processed as described in the succeeding sections.
3.2. Image Registration and Image Classification
Four images were registered by the use of ground control points. One of the four images has been used as the master image. The other images were then geometric registered to each other. ER Mapper software was used to do the supervised classification with Gaussian maximum likelihood algorithm.
3.3. Change Detection
Following registration and classification, two classified images were produced. One image was classified healthy grass without any change, and the other one was classified varnished grass. Post-Classification Comparison was then employed to detect the differences between the two images. Change maps have been complied to display the specific nature of the changes between the two classified images.
4. Outcome Analysis
4.1. Color Infrared Image
Images of healthy grass without any change and of varnished grass are shown respectively in Fig. 2 and Fig. 3. The white circles shown in these images are control points used in image registration. Healthy vegetation shown in Fig.2 reflects strong infrared wave before change. Some grass have been painted and then exposed dark red as shown in Fig.3. Because of the painted grass have low reflection in the IR region.
4.2. Supervised Classification
Classified images of healthy grass without any change and of varnished grass are shown respectively in Fig. 4 and Fig. 5. Four blue spot with red ring shown in these images are control points. Healthy vegetation appears to be green tone, painted grass shown in Fig.5 appears red tone, and unclassified pixels shown in both Fig.4 and Fig.5 appear to be black tone.
4.3. Change Detection Matrix
Post-classification change detection used in this research provides "from-to" information. A change detection matrix is shown as below. Note in the table that the pixels without change are located along the major diagonal of this matrix. Note also in the table that the producer's accuracy of painted grass class is only 20.29%, the omission error is 79.71%. The user's accuracy of painted grass class is 15.17%, the commission error is 84.83%. The overall classification accuracy of this error matrix is 62.82%. Kappa analysis is a discrete multivariate technique of use in accuracy assessment (Congalton and Mead, 1983). Khat computation incorporates the of-diagonal elements as a product of the row and column marginal. Computation of the Khat statistic may also be used to compare two similar matrices(consisting of identical categories) to determine if they are significantly different. The Khat computation of the error matrix is 17.34%, and it means that the two classified images have significantly difference.
| Categories
| Image categories without change (from)
|
Unclas-
sified pixel (0)
| Healthy grass (1)
| Painted grass (2)
| Control point (3)
| Total
|
Image
categories
with
change
(to)
| Unclas-
sified pixel (0)
| 51461
| 22922
| 2146
| 0
| 76529
|
| Healthy grass (1)
| 5018
| 6924
| 755
| 1
| 12698
|
| Painted grass (2)
| 2215
| 1931
| 755
| 76
| 4977
|
| Control point(3)
| 0
| 3
| 65
| 228
| 296
|
| Total
| 58694
| 31780
| 3721
| 305
| 94500
|
| Producer's Accuracy
| User's Accuracy
|
| Unclassified pixel = 51461/58694 = 86.21%
| Unclassified pixel = 51461/76529 = 67.24%
|
| Healthy grass = 6924/31780 = 21.79%
| Healthy grass = 6924/12698 = 54.53%
|
| Painted grass = 755/3721 = 20.29%
| Painted grass = 755 / 4977 = 15.17%
|
| Control point = 228/305 = 74.75%
| Control point = 228/296 = 77.03%
|
 |
where |
r is the number of rows in the matrix
|
| Xii is the number of observations in row i and column i
|
| Xi+ X+i are the marginal totals for row i and column i
|
| N is the total number of observations
|
=76529*58694+12698*31780+4977*3721+296*305 = 4913945263
(94500*59368-4913945263)/(94500
2-4913945263) = 17.34%
