Simularity dynamic range adjustment using histogram equalization

Dynamic Range Adjustment (DRA) increases the contrast in a set of images in order to reduce the variations from lighting and enhance variations due to actual changes. The result is an improvement in the performance of change detection and other related algorithms.

This algorithm works by converting each input image to HSB color space. A histogram is then generated from the Brightness component (B) and that histogram is equalized. It is important that the algorithm know the color depth of the image, which is not always apparent from the data format of the remote sensing image. The DRA algorithm computes this by measuring the brightest value in the image, and using it as a base to compute the color depth and this color depth is then used for the histogram equalization. Once the histogram is equalized in Brightness, the image is converted back to RGB using the equalized Brightness value. The results of this algorithm result in some information loss in the imagery to compensate for the enhanced contrast, which is intentional as it benefits image analytics that look for changes in imagery due to physical changes and not those due to lighting variations.

When temporal change detection algorithms, such as AIADS, analyze AOI’s which contain changing lighting due to shadows, the variation in pixel values is falsely higher which can mask actual changes when they do happen. This occurs more frequently in scenery with a high dynamic range. Some example scenes with high dynamic range include residential areas, industrial sites, and urban areas. Applying DRA to these AOI’s can increase the contrast so that minor variations are filtered out, resulting in higher true positive and lower false positive results. DRA is less important in scenery which already has a narrower dynamic range such as desert or ocean imagery. Thus, some advantageous use cases of DRA include oil spill detection at a refinery, rooftop damage detection, construction monitoring, and more.

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