Publications & International Conferences
IEEE TGRS Journal:
A Scalable Tile-based Framework for Region merging Segmentation
Processing large very high-resolution remote sensing images on resource-constrained devices is a challenging task because of the large size of these data sets. For applications such as environmental monitoring or natural resources management, complex algorithms have to be used to extract information from the images. The memory required to store the images and the data structures of such algorithms may be very high (hundreds of gigabytes) and therefore leads to unfeasibility on commonly available computers. Segmentation algorithms constitute an essential step for the extraction of objects of interest in a scene and will be the topic of the investigation in this paper. The objective of the present work is to adapt image segmentation algorithms for large amounts of data. To overcome the memory issue, large images are usually divided into smaller image tiles, which are processed independently. Region-merging algorithms do not cope well with image tiling since artifacts are present on the tile edges in the final result due to the incoherencies of the regions across the tiles. In this paper, we propose a scalable tile-based framework for region-merging algorithms to segment large images, while ensuring identical results, with respect to processing the whole image at once. We introduce the original concept of the stability margin for a tile. It allows ensuring identical results to those obtained if the whole image had been segmented without tiling. Finally, we discuss the benefits of this framework and demonstrate the scalability of this approach by applying it to real large images.
Large scale region-merging segmentation using the local mutual best fitting concept
Large scale segmentation remains a challenging task because of time and memory consuming. A usual strategy to process efficiently a large volume of data is to divide into chunks to be processed separately, either sequentially to reduce memory footprint or in parallel in order to speed up the computation. In image processing in general this boils down to dividing the input image into tiles. However, for image segmentation, the tile splitting usually leads incoherent segments on the borders of the tiles even when some overlap between the tiles is applied. In this paper we propose a new strategy making possible the tiling for image segmentation algorithms while maintaining the accuracy of the final results. Specifically, we focus on iterative region merging methods but the strategy can be extended to any segmentation algorithm. The introduction of the local mutual best fitting concept and the area of influence of a segment allows to establish a new methodology of segmentation based on three phases: the tile-based reduction, the iterative reduction and the completion of the segmentation. This new methodology was applied on a large Pleiades HR image with success proving the feasibility of the approach.
Awards & Communications
Award-winning presentation during the JC2 (Journées CNES Jeunes Chercheurs) at the Space city in Toulouse, France.
Encouraging future vocations is a mission of national importance that CNES takes very seriously. Every year, it awards 100 research grants to PhD and post-doc students. From 12-15 October at the Cité de l’espace space theme park in Toulouse, awards for the best work were presented at the JC2 young researchers forum. Pierre Lassalle, a PhD student at the CESBIO biosphere research centre, was one of the eight laureates. His research in the cutting-edge field of ‘big data’ is exploring and devising stable and efficient remote-sensing algorithms able to process large volumes of data. His findings are likely to remove the scientific obstacles for numerous applications including land- use mapping, feature detection and monitoring. The new algorithms will be available in the open-source OTB (Orfeo Toolbox) software developed by CNES.
Pléiades Days 2014:
Presentation of a poster.