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In remote sensing image analysis, change detection approaches typically compare two images captured by the same airborne or spaceborne sensor at different points in time. However, as airborne and spaceborne imaging platforms have become increasingly more accessible, the variety of sensor designs has grown in tandem. The ability to combine these multi-modal remote sensing images for change detection would provide a far more frequent view of the earth, but traditional approaches are challenged by the intrinsic data variation across sensor designs. The recently introduced multi-sensor anomalous change detection (MSACD) framework addresses this challenge by using a data-driven machine learning approach that can effectively account for differences in sensor modality and design, and does not require any signal resampling of the pixels. This flexible framework enables the use of satellite image pairs from different sensor platforms. Here, we perform experiments to further evaluate the efficacy of the MSACD change detection framework; these experiments include augmenting the images with engineered features that seek to increase the mutual information of the image backgrounds and, in turn, better emphasize the anomalous changes. While these initial results are demonstrated on same-sensor spectral data, the experiments naturally extend to the multi-sensor domain.
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