You are here

Generalized Change Detection to Cue Regions of Interest



OBJECTIVE: Using frequent revisit imagery, determine when a significant change has occurred that warrants further analysis, using a representation of a model of significance that can be specified on a sliding scale. 

DESCRIPTION: Overhead imagery, such as small satellite imaging systems, will soon be able to provide imagery of the entire planet on a regular recurring basis (daily, or better). It will not be possible for human analysts to look at but a tiny fraction of this imagery. Pixel-based change detection based on registration of subsequent images of the same terrain can indicate changes, but most changes are not significant, and instead are due to lighting changes, minor misregistration, and normal variations of no significance. When known objects are sought, automated recognition systems might be able to detect changes in the presence of objects of interest in the latest image, to cue further collection or analysis, provided the objects of interest are sufficiently large. However, to detect situations that are not expected but are unusual, and have particular value to the user, a means to cull potential images based on significant changes from prior collects is needed. This topic is based on a hypothesis that it should be possible to devise algorithms, or to train an algorithmic process, to detect changes that have significance, with a false alarm rate that might be high, but nonetheless can reduce the analytic load requiring further analysis by orders of magnitude. As a cuing technique, it is desirable that the detection rate be very high. Significant changes are not likely to involve large areas of changes in brightness or color, but rather more likely involve confluence of large numbers of vehicles, or presence of a large vehicle where one is not expected, or initiation of a new construction site or clearing of a field to change the type of coverage, or grading for construction of a road or railway, or any of a number of other changes that could be of interest. Typically, the cuing of further analysis will trigger the collection of new imagery at higher resolution, or collection of other evidence, at the designated location. The further analysis might involve automated systems applied to that new evidence, or might require a human analyst to look at the totality of data at that site. Regardless of how the output is to be used, the goal is to dismiss massive amounts of imagery where no significant intelligence can be gleaned, so as to reduce collection and analysis workloads. The significance of changes depends on the purpose one has in examining the imagery in the first place. Ideally, the system can be trained to discern significant changes by being trained against a carefully curated set of examples of changes that indicate the kinds of changes that the user seeks. The method for discerning changes might be dependent on the geolocation of the imagery, providing the method can be codified in relatively few parameters, or the method might be dependent on a characterization of the type of terrain represented by the imagery (field, urban, forest, etc.). Whatever method is proposed, it should incorporate a sliding scale that allows one to specify the degree of significance of the desired changes, thereby sacrificing detection rate for greater culling of less significant changes. 

PHASE I: Develop an approach and provide evidence of its viability to detect significant change detection through experiments using test pairs of before/after images that include both significant change and lack of significant change. 

PHASE II: Develop an efficient implementation and evaluate performance characteristics according to well-defined metrics to detect significant change and to cull large amounts of imagery data. 

PHASE III: As overhead and airborne imagery become more common, commercial as well as military applications will require automated systems to sample out regions of interest, dependent on the particular mission and needs of the user. An ability to train a system to extract imagery data of relevance to the user could become a service offered to organizations with geospatial intelligence requirements. 


1: I. Niemeyer, M. Canty, "Pixel-based and Object-oriented change detection analysis using high resolution imagery," Research Gate (accessed 1/12/2017), Jan 2003.

2:  S. Xiaolu, C. Bo, "Change Detection Using Change Vector Analysis from Landsat TM Images in Wuhan," Procedia Environmental Sciences, Vol 11A, 2011, 238-244, Science Direct, (Accessed 1/12/2017).

3:  H. Shin, M. Orlon, D. Collins, S. Doran, M. Leach, "Stacked autoencoders for unsupervised Feature Learning and Multiple Organ Detection in a Pilot Study Using 4D Patient Data," IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) Vol. 35(8), pp. 1930-1943, Aug 2013,

KEYWORDS: Change Detection, Autoencoding, Imagery Analysis, Imagery Culling, Geospatial-intelligence 


Robert Hummel 

(571) 558-4608 

US Flag An Official Website of the United States Government