Towards the automatic selection of optimal seam line locations when merging optical remote-sensing images |
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| Authors: | Le Yu Eun-Jung Holden Michael Charles Dentith Hankui Zhang |
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| Affiliation: | 1. Institute of Space Information Techniques, Department of Earth Sciences , Zhejiang University , 310027 , Hangzhou , PR China naisoild@gmail.com.;3. Centre for Exploration Targeting, School of Earth and Environment, The University of Western Australia , Crawley , Western Australia , 6009 , Australia;4. Institute of Space Information Techniques, Department of Earth Sciences , Zhejiang University , 310027 , Hangzhou , PR China |
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| Abstract: | Image mosaicking is an important task in remote sensing due to the need for imagery with a greater spatial extent than provided by individual scenes. Merging of images requires the selection of a seam line within their area of overlap along which the scenes are merged. The seam line has a better chance of being invisible if it lies in regions where the images to be merged are very similar. The automatic detection of an optimal seam line is not a trivial task as it is difficult to find laterally continuous regions with high image similarity, and to identify image similarities when there are variations in the images, for example due to different illuminations or viewing directions, or shadow differences of tall structures. This article presents an automatic seam line location technique for remote-sensing images and achieves the following three objectives: to trace along the locations with minimal image difference so that the merged data set appears seamless; to avoid creating discontinuities within salient features within the images; and to ensure that the more accurate radiometric values that are associated with the least distance from the nadir point are better preserved in the mosaic image. Therefore, our method uses pixel-based image similarity measurement to choose the locations with high colour, edge and texture similarity; a region-based saliency map that is based on a human attention model to identify and avoid the areas with visibly dominant foreground objects; and location preference to encourage the seam line to lie as close as possible to an equal distance from the two nadir points of the images being merged. These measures are used as input to a cost function and the estimated costs are used to guide the tracing of the seam line in a dynamic programming algorithm. Our experiments demonstrate that the consideration of a combination of factors produces superior results to using just one or two of the variables as controls when merging high-resolution images containing complex structures. |
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