Entry Name: "VRVis-Matkovic-MC3"

VAST Challenge 2017

Mini-Challenge 3



Team Members:

Michael Beham, VRVis Research Center, Vienna, Austria, Beham@VRVis.at PRIMARY

Rainer Splechtna, VRVis Research Center, Vienna, Austria, Splechtna@VRVis.at

Silvana Podaras, VRVis Research Center, Vienna, Austria, podaras@vrvis.at

Denis Gracanin, Virginia Tech, Blacksburg, VA, USA, gracanin@vt.edu

Kresimir Matkovic, VRVis Research Center, Vienna, Austria, Matkovic@VRVis.at


Student Team: NO


Tools Used:

Matlab scripts: data processing and determining differences between images.

Octave scripts: data processing and images processing, e.g., edge detection.

GIMP: image processing.

Google calculator: numerical computations.


Approximately how many hours were spent working on this submission in total?



May we post your submission in the Visual Analytics Benchmark Repository after VAST Challenge 2017 is complete? YES







MC3.1 Boonsong Lake resides within the preserve and has a length of about 3000 feet (see the Boonsong Lake image file). The image of Boonsong Lake is oriented north-south and is an RGB image (not six channels as in the supplied satellite data). Using the Boonsong Lake image as your guide, analyze and report on the scale and orientation of the supplied six-channel satellite images. How much area is covered by a pixel in these images? Please limit your answer to 3 images and 500 words.

We identify SIFT Features in RGB sensor data using Grayimage, pattern in RGB GrayImage and RANSAC Algorithm (Figure 1). Guide image has the same orientation like the pattern.

Figure 1: Feature matching. Image registration is done using SIFT features + RANSAC. That results in a transformation matrix. We use different scale parameter for SIFT features to detect enough features.

We use the shape of the lake to refine the transformation matrix (Figure 2).

Figure 2: Refining the measurement.

We measure approximately 30 pixels for the lake which is equivalent to 3000 feet. Therefore, the resolution is 3000 feet / 30 pixels which is equivalent to 100 feet/pixel (30.48 meters/pixel).

The size of the satellite images is 651 pixels (both height and width) * 100 feet/pixel which is equivalent to 65100 feet or 12.33 miles (both height and width).


MC3.2 Identify features you can discern in the Preserve area as captured in the imagery. Focus on image features that you are reasonably confident that you can identify (e.g., a town full of houses may be identified with a high confidence level). Please limit your answer to 6 images and 500 words.

We start by using RGB images (Figure 3), false-colored images (Figure 4) and vegetation indices (Figure 5) and then apply ratio transformation (Figure 6).

Figure 3: RGB images.


Figure 4: False-colored images.


Figure 5: Vegetation indices.


Figure 6: Ratio transformations.


The identified features are shown in Figure 7. We identified it using false-colored images and measurements like NDSI. Figure 8 shows a questionable feature that indicates freezing areas on a lake surface (freeze hole).

Figure 7: Identified features.

Figure 8: Freeze hole.


MC3.3 There are most likely many features in the images that you cannot identify without additional information about the geography, human activity, and so on. Mitch is interested in changes that are occurring that may provide him with clues to the problems with the Pipit bird. Identify features that change over time in these images, using all channels of the images. Changes may be obvious or subtle, but try not to be distracted by easily explained phenomena like cloud cover. Please limit your answer to 6 images and 750 words.

Some easily detected phenomena include cloud cover (and cloud shadow), seasonal changes (e.g. snow, changes in vegetation), and sensor artefacts.

Preprocessing requires generating cloud and shadow maps (Figure 9).

Figure 9: Cloud and shadow maps.

We compare satellite images captured at similar time of the year. Comparison by frame differences is done by calculating differences for visible and invisible band combinations and then setting the differences, resulting by clouds and images, to 0. Focus is on the interesting parts of the image (Figure 10).

Figure 10: Frame differencing - creating the difference image.

The resulting changes between 24 August 2014 and 6 September 2016 are shown in Figure 11.

Figure 11: Changes between 24 August 2014 and 6 September 2016.


Seasonal vegetation changes between 26 June 2016 and 6 September 2016 are shown in Figure 12.


Figure 12: Seasonal vegetation changes between 26 June 2016 and 6 September 2016.

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