Land Cover Change Detection Radio metrically-Corrected
Multi-Sensor Data
Calculation of the transformation matrix
A computer program to implement the Gramm-Schmidt Orthogonalisation described by Jackson (1983) was developed. The program requires an input file containing the initial value for the transformation coefficients was
produced and shown Table 2. The transformation coefficients were later used to transform input data to new N-dimensional space. The result of the transformation were uncorrelated component images.
Table 2: Transformation coefficients of the Gramm-schmid orghogonal.
| band |
Gs1 |
Gs2 |
Gs3 |
Gs4 |
Gs5 |
| XS1 |
0.26095 |
-0.18073 |
-0.24689 |
0.04577 |
-0.19943 |
| XS2 |
0.54687 |
-0.31396 |
-0.47537 |
0.48842 |
0.06547 |
| XS3 |
0.31172 |
-0.55726 |
-0.51663 |
-0.56281 |
-0.00233 |
| TM2 |
0.30715 |
-0.14964 |
0.23175 |
-0.21913 |
0.87124 |
| TM3 |
0.60050 |
-0.18355 |
0.53798 |
-0.31704 |
-0.44091 |
| TM4 |
0.28413 |
0.70862 |
0.32098 |
0.54228 |
0.04975 |
Result and Discussions
The GSO transformation process produces a file containing five component images from the original six channels input data. This is in accordance with Jackson (1983) and Mather (1987), in that the process should result in N-1 component images, where N is the number of spectral band in the input data. Based on these coefficients, the five GSO component images were produced and these are shown in Figure 1.
Figure 1: Component images derived from Gramm-Schmidt Orthogonalisation techniques.
The transformation coefficient (Table 2) shows that all spectral bands contribute positive values in the first component (gsl), within which each of the red channels of both data sets contribute more than 50% to the component loading. Examining visually of the first visually of the first component image (gsol), three major cover types, namely, water bodies, vegetative cover and non-vegetative covers can easily be discriminated. In the image, water bodies appears dark in tone, vegetative cover in grey tones and non-vegetative in bright tones. In the vegetative area, it also found that there is variation in grey level values, which may due to different proportions of ground cover. However, the type of vegetative cover as well as change in vegetation cannot visually be discriminated in this component.
There is a distinct difference in the contribution of component loading between visible and near-infrared channels observed in the second component (gs2). The visible channels of both data sets contribute low and negative loading whereas the near-infrared channels contribute high and positive loading to the component vector. The component image (gso2) exhibits high visual contrast between two distinct land surface features; vegetative and non-vegetative
covers. The non-vegetative cover, which include water bodies, urban area and bare appears dark in the image data but cannot be distinguished visually. The vegetative cover appears grey with various degrees of brightness. From its appearance, this component resembles a vegetation index image and may be used to discriminate non-vegetative against vegetative areas. However, similar to gsol, land cover type and potential change in its cover cannot be seen in this component.
