Divide And Segment - An Alternative For Parallel Segmentation

Remote sensing images with large sizes are usual. They also include several spectral channels, increasing the volume of information. To get valuable information from data automatically, computers need higher amounts of memory and efficient processing techniques. Segmentation is a key technique to deal with remote sensing. It identifies regions in images. Therefore, it deals with large amounts of information. Even with current computational power, some image sizes exceed the memory limits, which need different solutions. An alternative to overcome such limits is to employ divide and conquer strategy, splitting the image into tiles, and segmenting each one individually. However, arises the problem of merging neighboring tiles and keeping the homogeneity in such regions. In this work, we propose an alternative to create the tiles, by defining noncrisp borders between tiles, but adaptive borders for the tiles. By applying our method, we avoid the postprocessing of neighboring regions, and therefore speed up the final segmentation.