Application of Change Detection Algorithms for Mine Environment Monitoring
Pre-processing
Geometric rectification. Aquadratic ( second-order) transformation was performed on ground control points (GCPs) from the may 1988 SPOT XS image, given a root-mean-square error of ±1.24 pixels .pixel size was maintained equal to SPOT XS's normal 20m x 20m. Resampling was done using nearest neighbor so as minimize radiometric degradation.
Radiometric Correction. Dark pixel correction was used because data necessary to quantify the effects of atmosphere and instrument error on recorded digital numbers (DNs) were not available. Dark pixel subtraction is effected on each band by subtracting from all(DNs) the minimum DN for each band, the latter assumed an area on the ground with zero or near reflectance.
Image Rotation. In both Dizon images, solar illumination was coming from the sourtheast direction, resulting in topographic inversion when viewing the images north upwards. To facilitate visual interpretation, the image was rotated 180 degrees. The rotated 1988 and 1991 Dizon images are shown in figure 2 and figure 3 respectively. When referring to location maps, it should be noted that the upper part of the rotated Dizon image is actually oriented southward and the left side oriented eastward.
mage Registration and Radiometric Calibration
The December 1991 SPOT XS image was co-registered with the geometrically corrected May 1988 SPOT XS image. However, the two images have almost the same solar azimuth and elevation, allowing for reasonable radiometric calibration in lieu of absolute calibration using meteorological and other physical data.
Significant Between-Data Events
Significant physical events occurred in the study between the dates of acquisition of the two SPOT XS imagery used in multitemporal analysis. Mt. Pinatubo erupted in June 1991,ejection an estimated 1 billion cubic meters of ash and phroclastic material that blanketed much of the surrounding area. Ash fall damaged mine facilities and equipment, significant enough lower mine production in 1991. Also, rainfall levels, after along drought, were returning to normal and triggering lahar's flow. Lahar's flow blocked Mapanuepe River, flooding the immediate downstream of Dizon.
Change Detection by visual Comparison
Many change features are discernible from a visual comparison of theDec. 1991 with the May 1988 images. The effects on the study area of the Mt. Pinatubo eruption. Lahar flows. Ash fall, and the flooded Mapanuepe River show very well. A cursory inspection of the mine area shows that pit has become deeper, although other mine features do not exhibit any conspicuous change from the may1988 scene.
Change Detection by Image Differencing
A direct approach to change detection is mage differencing-pixel of the earlier image are subtracted from those of a co-registered more recent one. Mathematically, the procedure can be expressed as : Dx
ilk=X
ilk(t2)- X
ilk(t1)+c where X
ilk =DN fro band k,i and j are line and pixel number in the image, t
1=first date , t
2= second date and C=offset to produse positive DNs ( Singh,1989).
Band by band difference images for the Dec 1991 and May 1988 SPOT XS images were generated with one resulting single channel (NIR) difference image shown in figure 4. The more obvious changes are indcated as very light or very dark feature in the difference images are listed in table 1. Some features are unique in the Dec.1991 image while other are unique in the May 1988 image. Mid-grey pixel indicate relatively time-invariant features.
Image -to- Image Ratio
When ratioing co-registered multitemporal images band by band, we compute Rx
ilk=[X
ilk(t
1)]/[ X
ilk(t2)] where X
ilk (t
2) is the pixel value of band k for pixel x at row i and column j at time t
2 (Singh, 1989). For two absolutely calilutely calibarated multitemporal images, non-change pixels would have a ratio value of unit (1) (no scale factor applied). For non-calibrated image, the ratio for non-change pixel would deviate from unity.
Ratio of the Dec.1991 to the May 1988 Dizon data sets appear similar to the difference images but ratio images appear sharper and the degree of contrest between change and non-change features better indicates the degree of change.
Change Detection by Principal Analysis
The May 1988 and Dec. 1991 SPOT-HRV co-registered Dizon data were merged into one file and a standardized Principal Component Transformation (PCT) using eigenvector. loading derived from the correlation matrix was performed to produce six principal component (PCs). These are show as a single-channel images in figure 5 to 10. To aid in the analysis of information content , the eigenvectors loadings for each principal component are plotted and shown in figure 11. Values of eigenvectors and per qualitatively classified as either bright, medium or dark in each principal component and listed in Table3.
The eigenvectors show that PCI is a combination of all high positively loaded visible bands and subdued NIR. Thus features which are bright in the visible bands of both the May 1988 and Dec. 1991 images are highlighted. Because the corresponding bands have almost the same eigenvectors, PCI in effect does not offer much in discrimination of change features.
