## 2 Change Detection with GeoCDX  
The Geospatial Change Detection and Exploitation System (GeoCDX) is a sensor-agnostic change detection system for high resolution remote sensing imagery [2]. GeoCDX automatically ingests imagery from a variety of sensors, including IKONOS, QuickBird, GeoEye-1 and WorldView-2. Once ingested into the system's catalog, a data-specific processing plan is developed based on the characteristics of the imagery. The first step of this processing may involve steps, such as geometriccorrection and conversion to top-of-atmosphere (TOA) reflectance, if they are appropriate for the imagery. The system automatically determines which temporal pairs of images can be created when new imagery is ingested; this also creates a processing plan for each pair. This fully-automated plan includes image-to-image coregistration, radiometric balancing, high-level feature extraction, differencing of the extracted features and fusion of the difference images into a single change confidence image. A summary of these processing steps can be found in Figure 1.  
The change detection results are then subdivided by the GeoCDX system into \(256\,\times\,256\)-meter tiles. A per-tile change score is then calculated for each tile based on the extent and intensity of the change. As defined in [2], this per-tile change score is:  
\[\frac{1}{|T|}\left(\sum_{\forall(i,j)\in T}s_{ij}\right) \tag{1}\]  
where \(T\) is the set of pixels that compose a tile and \(s_{ij}\) is a per-pixel change score calculated using a non-linear stack filter algorithm that accounts for the intensity and morphological characteristics of the change present at each pixel. This per pixel-change score is described in detail in Section IV.B. of [2]. **Figure 2.** In the GeoCDX web user interface, the far left-hand side contains the navigation menu for the GeoCDX software. Immediately to the right of that are clickable links to sets of change detection results in batches of twenty tiles (_i.e._, 1-20, 21-40, _etc._). Further to the right are three images in each row representing the before image, the after image and the corresponding change map that highlights changed regions. Finally, on the far right side of each row, there is a UI element that allows an analyst to tag a tile as \"change\" (the button with the red text) or \"no change\" (the button with the green text). Tiles are then ranked using this change score from most change to least change and presented in rank order in a web interface. Users are then free to exploit the tiles that have been determined to have the most change and stop their analysis when they no longer find a relevant change in the results. An example of highly-ranked change results in the GeoCDX web interface can be seen in Figure 2.  
Additionally, the GeoCDX system also uses the per-tile feature signature and per-tile change signature to cluster tiles based on their amount of change and the content. Complete details on the competitive agglomerative clustering algorithm used for this task can be found in [19]. This algorithm produces a dynamic (but bounded) number of clusters based on the degree of variance in the types of change present in a given pair. Each cluster produced represents a distinct type of transition between land-cover, land-use types. For example, one cluster may represent grassland that has changed to residential housing, while another may contain examples of new buildings appearing in urban areas. Figure 3 shows several examples of members of clusters produced by the GeoCDX system. Figure 3: Each column depicts a representative example from a particular change cluster in Columbia, MO, USA. Notice that each cluster depicts a different type of change. (**a**) Cluster 2 before. (**b**) Cluster 5 before. (**c**) Cluster 7 before. (**d**) Cluster 12 before. (**e**) Cluster 2 after. (**f**) Cluster 5 after. (**g**) Cluster 7 after. (**h**) Cluster 12 after. (**i**) Cluster 2 change. (**j**) Cluster 5 change. (**k**) Cluster 7 change. (**l**) Cluster 12 change.