Filters for the enhancement of lineaments, ‘high pass filters’

The so-called ‘high pass’, ‘edge enhancement’ or ‘sharpening’ filters are used for lineament detection. For hydro-geologic study the high pass or gradient filters are mostly used.

The functioning of a filter can be understood through the following exercise. Consider 8 pixels on a row; the first 4 have a DN of 1 due to reflectivity of land cover X, and the last four a DN of 4, of land cover Y. A so-called

‘operator’ can be applied to the row, and the operator may consist of the values -1 and 1. First the DNs of the first two pixels are multiplied with the operator and the sum is written at the second position; -1 *1 + 1 *1= 0. Then the operator is shifted to pixels no. 2 and 3, then to 3, 4, and so on. It is easy to see that as long as the DNs remain the same, the result will be 0. However, when there is a change (an edge) in the example from pixels 4 to 5 the result will be different: -1 *1 + 1* 4 = 3, but will be zero again for the next pairs of pixels, as is shown below.

1 1 1 1 4 4 4 4 Original image

0 0 0 3 0 0 0 Filtered image

One could decide to assign black to the value 0 and white to the value 3. The effect is that the edge marking the change from land cover X to Y is shown as a white line in a uniform dark image. The original image is thus trans-formedinto a new one.

For image processing usually a 3 x 3- or 5 x 5-filter is used. The method of calculation is the same as given above, but the sum of the products is written on the central pixel. The filter then moves a pixel in x-direction and the calculation is repeated until the end of the row. The procedure is continued beginning with the first pixels of the next row, and so on.

Operators and their effects are illustrated in Figure 2.11:

Operator awith two terms, shows the effect as explained above.

Operator bhas the same effect, but since 3 terms are used the edge effect comprises 2 pixels.

Operator cresults in a kind of shadow effect along enhanced contrast in the image.

Operator dretains much of the original image and enhances the contrast somewhat.

Edge enhancement filter

For the study of lineaments, operator (d) has the advantage that most of the original image content is retained, showing the terrain, but contrast is increased somewhat and edges are a little sharpened. Instead of the operation on one row of pixels, the filter is used for blocks of three rows and three columns, for example in a symmetrical fashion to avoid giving a-priori preference to any direction:

-1 -1 -1

-1 16 -1

-1 -1 -1

Figure 2.12a shows a small part of the eastern fault zone of the Central Ethiopian Rift, north of Lake Langano (the lake is not completely shown, it is just south of the area depicted). The above filter is selected because the fault scarps run more or less north south, while the sun at the time of recording of the image is at about 9.30 am, thus at a relatively low angle in the east, so that the scarps are in shadow. Because of the steep slope of the scarp, they have a fairly dense vegetation cover, which gives contrast to the cultivated areas or grazing lands in the gentle sloping to flat areas in between the fault scarps. Because of these two reasons the features as seen on the original image have to be retained in the image processing and by using the above filter only a little ‘sharpening’ is done.

Along the extension of one of the faults shown on the image near the shore of Lake Langano, a powerful spring was found. Interestingly, the local inhabitants said the spring (s) provided minimal water in the period prior to the 1970s. It thus seems that recent tectonics widened the fault plane and increased its capacity as a conduit of water from a river, which feeds marshes in a minor graben. Figure 2.12b indicates that faults are running from the marshes to the spring. Ayenew (2003) found that isotopic composition of the spring water was similar to that of the river. Faults of open nature are expected in a rift environment.

Sketch illustrating effect of high pass filters (see text)

Figure 2-11

Filter giving full transformation of the image

The situation is quite different in the southwestern part of the Central Ethiopian Rift, because the geology is dif-ferent from that of the above example. Figure 2.13 (upper left) shows the area west of Lake Shala and it is difficult to detect lineaments. The figure shown is a black and white image of band 5 (infrared) of TM, being the image that displays lineaments best compared to the other bands, in this case, or even to false colour images comprising com-binations of three bands. Because of the difficulty, a strong transformation method can be used like a Laplace filter, with operators such as (a) to (c), shown in Figure 2.11 (upper right). Written in a 3 x 3 format:

0 -1 0

-1 2 -1 0 -1 0

Because the filtered product is fully transformed, the histogram is entirely different from the original image and thus stretching has to be done. Automatic stretching between 1% and 99% may not give the best image for lineament interpretation and some experimentation with different cutoffs should be undertaken before interpre -tation starts. Note that little of the original contrasts, due to vege-tation, rocks/soils, shadows, and so on are still present in the image, but any local contrasts in reflection values (DNs) have been enhanced. This product has been interpreted visually (the only practical way to extract lineaments), shown in Figure 2.13 (lower left).

The hydrogeologic significance can be briefly sketched as follows: the groundwater contour map of the area shows a gradient to the south and the east. Pumping tests have shown that the aquifer, composed of different rocks, behaves as a hard rock aquifer, hence poor primary porosity. There can be appreciable loss of water by frac-ture flow through the north-south trending lineaments and less loss of water through fracfrac-tures/faults in easterly direction to Lake Shala.

TM band 5 of a part of the central Ethiopian Rift, with edge enhancement filter, which sharpens contrast only a little.

Distance E-W is 37 km.

Figure 2-12a Lineament interpretation, mainly

tensional rift faults, using

on-screen digitising with enlargement.

s= spring, see text Figure 2-12b

Other filters

The strength of the local spectral gradient is enhanced by applying the operator in two directions (in x and y direction). The operators of some commonly used zero-sum gradient filters are shown below:

-1 -1 -1 1 0 -1

Prewitt x→ 0 0 0 y→ 1 0 -1

1 1 1 1 0 -1

-1 0 1 1 2 1

Sobel x→ -2 0 2 y→ 0 0 0

-1 0 1 -1 -2 -1

0 0 0 0 -1 0

First derivative x→ -1 0 1 y→ 0 0 0

0 0 0 0 1 0

0 0 0

Laplace x & y 1 -8 1

1 1 1

Use of high-pass filter for interpretation of lineaments.

Area west of Lake Shala, Central Ethiopian Rift valley.

Upper left: Landsat ETM band 7 Upper right: Same area filtered

Lower left: Lineaments (thin lines and dashed lines) and former shorelines of Lake Shala during Pleistocene expansion stage (thick lines).

Distance E-W is 12.1 km.

Figure 2-13

A non-filtered LANDSAT-ETM Panchromatic band with a resolution of 14.5 m of a granite area with inselbergs and other outcrops and some dolerite dykes is shown in Figure 2.14a. The regolith has generally limited thick-ness. Most of the drainage follows fractures. The dark area in the centre is a small reservoir. Figures 2.14b–d show the effect of the above filters.

High pass filters with a 5 x 5 kernel have also been used, but these may result in a blurring effect of sharp gradients.

The Laplace filter calculates gradients in both x- and y-directions (second partial derivative). The effect of using the Laplace filter is that when the value of the central pixel is relatively high compared to its neighbours, the

Granite area in central India of little weathered terrain, some inselberg complexes drainage adjusted to fractures and fractures. LANDSAT ETM Pan chromatic band

Figure 2-14a Same area and same band as left hand

Figure 2.14a, filtered with Prewitt filter, Y direction.

Distance E-W is 7.2 km.

Figure 2-14b

First derivative filter, X direction

Figure 2-14c Figure 2-14d Sobel filter, X direction

value assigned to the central pixel will be even lower than its neighbours and vice versa. Thus values, which

‘largely’ differ from their neighbours, come out as an opposite in the output map, while areas with more or less the same pixel value ‘disappear’. The Laplace filter is quite sensitive to noise.

It must be remembered that most of the common multi-spectral images (e.g. SPOT, Landsat, IRS) are recorded at around 9.30 a.m. to avoid clouds in tropical areas and to enhance morphology, as at this time the angle of the sun is relatively low. However, topographic shadow effects will benefit lineaments that run more or less perpendicular to the azimuth of the sun and attention must be paid to this fact during interpretation.