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Volume: 32 | Article ID: art00005
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LambdaNet: A Fully Convolutional Architecture for Directional Change Detection
  DOI :  10.2352/ISSN.2470-1173.2020.8.IMAWM-114  Published OnlineJanuary 2020
Abstract

Change detection in image pairs has traditionally been a binary process, reporting either “Change” or “No Change.” In this paper, we present LambdaNet, a novel deep architecture for performing pixel-level directional change detection based on a four class classification scheme. LambdaNet successfully incorporates the notion of “directional change” and identifies differences between two images as “Additive Change” when a new object appears, “Subtractive Change” when an object is removed, “Exchange” when different objects are present in the same location, and “No Change.” To obtain pixel annotated change maps for training, we generated directional change class labels for the Change Detection 2014 dataset. Our tests illustrate that LambdaNet would be suitable for situations where the type of change is unstructured, such as change detection scenarios in satellite imagery.

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Bryan Blakeslee, Andreas Savakis, "LambdaNet: A Fully Convolutional Architecture for Directional Change Detectionin Proc. IS&T Int’l. Symp. on Electronic Imaging: Imaging and Multimedia Analytics in a Web and Mobile World,  2020,  pp 114-1 - 114-7,  https://doi.org/10.2352/ISSN.2470-1173.2020.8.IMAWM-114

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