However, the possibilities are virtually endless. You can use change detection to analyze virtually any aspect of "then versus now" or "before versus after".
You can perform change detection with both airphoto and satellite imagery, and you can choose from several options on how you want the changes to be shown. That is, areas of change can be shown using one of three available options for output display: red, green, and blue (RGB), pseudocolor, or grayscale.
To perform change detection, you select a working image and a reference image. The working image is typically the more recent image. The reference image is the older image.
With each image—working and reference—you can select whether to compare all layers in the image, or select a specific layer. When you compare all layers, each channel in the file is used as input data, and you must specify the same number of channels as input for both files; otherwise, an error will occur. The channels are compared sequentially (difference: A1-B1, A2-B2; or ratio: A1/B1, A2/B2) before being amalgamated into the output change layer.
The Difference method compares images of the same location from different dates, and then subtracts the imagery of one date from that of the other.
The Ratio method compares the pixel-by-pixel ratio of the data from two registered images. Pixels that show no change will have a value of one, while pixels that changed will have a higher or lower value.
In addition to the type of change detection, you can select whether to use the absolute value, percentile, or both. The absolute value is, as the name implies, the fixed value of the computed change layer. The magnitude of change is reported, but not the direction.
Percentile produces output values on a consistent scale, regardless of the input-pixel values. It stretches the output change layer from a range of zero to 100. When used in combination with absolute value, an output value of zero represents the pixels that changed the least, and 100 corresponds to the pixels that changed the most.
If necessary, you can overwrite the results of a previous change detection. That is, when a change detection has been run previously on an image set, you can choose to overwrite the results with those of the active run, rather than create a new layer. This is recommended to minimize cluttering the tree on the Maps tab in Focus with unwanted layers.
Another handy feature of change detection is the option of using masks. You can use a mask to limit the input data to process. That is, a mask can define one or more areas to include, such as the pixels under the inclusion mask. For example, when you are interested only in changes in a specific area, you can use an inclusion mask to limit the change detection to that area.
You can also use a mask to exclude one or more areas. That is, with an exclusion mask pixels under the mask are not processed. For example, you can use an exclusion mask over a water body when any variation of pixels in that area are of no interest.
You can use an inclusion mask, exclusion mask, or both.
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