Module 6 - Remote Sensing - Spatial Enhancement

Enhancement of Landsat 7 Imagery with Fourier Transform and
3x3 sharpening, ERDAS Imagine v.2014

In this week's Remote Sensing Lab, we tried out various digital filters in order to enhance imagery.  High-pass filters can be used to bring out more detail and contrast in an image, while low-pass filters smooth the imagery and emphasize larger-scale features.

The larger image to the left is an example of how imagery that has been corrupted by missing data can be improved.  We used Fourier Transform in ERDAS Imagine to partially blend the values of pixels in the black stripes that show up in the original image (left) into the pixels surrounding them.  This processing was followed by convolution filtering in ERDAS.  Here, we make a "kernel" of 8 pixels that will surround each pixel in the image in turn.  In filtering with a kernel, the values of the 8 pixels surrounding each pixel are averaged, and that value is then applied to the center pixel.  This serves to de-emphasize the black stripes.  Finally, radiometry was adjusted in ERDAS Imagine, by way of the LUT histogram breakpoints.

In the final enhanced image, the stripes are fainter, but still visible.  The main disadvantage to this type of processing is that the image can become very blurry and lose a lot of detail if the analyst is not careful.