Abstract
Remote sensing images with large spatial dimensions are usual. Besides, they also include a diversity of spectral channels, increasing the volume of information. To obtain valuable information from remote sensing data, computers need higher amounts of memory and more efficient processing techniques. The first process in image analysis is segmentation, which identifies regions in images. Therefore, segmentation algorithms must deal with large amounts of data. Even with current computational power, certain image sizes may exceed the memory limits, which ask for different solutions. An alternative to overcome such limits is to employ the well-known divide and conquer strategy, by splitting the image into chunks, and segmenting each one individually. However, it arises the problem of merging neighboring chunks and keeping the homogeneity in such regions. In this work, we propose an alternative to divide the image into chunks by defining noncrisp borders between them. The noncrisp borders are computed based on Dijkstra algorithm, which is employed to find the shortest path between detected edges in the images. By applying our method, we avoid the postprocessing of neighboring regions, and therefore speed up the final segmentation.
The original version of this chapter was revised: The copyright line was incorrect. This has been corrected. The Erratum to this chapter is available at DOI: 10.1007/978-3-319-02895-8_64
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Adams, R., Bischof, L.: Seeded region growing. Pattern Analysis and Machine 16 (1994)
Baatz, M., Schape, A., Schäpe, M.: Multiresolution segmentation: an optimization approach for high quality multi-scale image segmentation. In: Wichmann-Verlag (ed.) XII Angewandte Geographische Informationsverarbeitung, pp. 12–23. Herbert Wichmann Verlag, Heidelberg (2000)
Bader, D., Jaja, J., Harwood, D., Davis, L.: Parallel algorithms for image enhancement and segmentation by region growing with an experimental study. In: Proceedings of International Conference on Parallel Processing, pp. 414–423 (1996)
Bagli, V., Fonseca, L.: Seamless mosaicking via multiresolution analysis and cut line definition. In: Signal and Image Processing. ACTA Press (2006)
Bielski, C., Grazzini, J., Soille, P.: Automated morphological image composition for mosaicing large image data sets. In: IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007, pp. 4068–4071 (2007)
Bins, L., Fonseca, L., Erthal, G., Ii, F.: Satellite imagery segmentation: a region growing approach. Simpósio Brasileiro de Sensoriamento Remoto 8, 677–680 (1996)
Blaschke, T.: Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing 65(1), 2–16 (2010)
Bradley, B., Jacob, R., Hermance, J., Mustard, J.: A curve fitting procedure to derive inter-annual phenologies from time series of noisy satellite NDVI data. Remote Sensing of Environment 106(2), 137–145 (2007)
Câmara, G., Souza, R., Freitas, U., Garrido, J., Li, F.: Spring: Integrating remote sensing and gis by object-oriented data modelling. Computers and Graphics 20(3), 395–403 (1996)
Dial, G., Bowen, H., Gerlach, F., Grodecki, J., Oleszczuk, R.: IKONOS satellite, imagery, and products. Remote Sensing of Environment 88(1-2), 23–36 (2003)
Dijkstra, E.: A note on two problems in connexion with graphs. Numerische mathematik, 269–271 (1959)
Falcão, A., Stolfi, J., Lotufo, R.: The Image Foresting Transform: Theory, Algorithms, and Applications. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(1), 19–29 (2004)
Fayyad, U., Shapiro, G., Smyth, P.: The KDD process for extracting useful knowledge from volumes of data. Communications of the ACM 39(11), 27–34 (1996)
Goodchild, M.F.: Geographic information systems and science: today and tomorrow. Annals of GIS 15(1), 3–9 (2009)
Happ, P., Ferreira, R., Bentes, C., Costa, G., Feitosa, R.: Multiresolution segmentation: a parallel approach for high resolution image segmentation in multicore architectures. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (2010)
Haralick, R., Shapiro, L.: Image segmentation techniques. Applications of Artificial Intelligence II 548, 2–9 (1985)
Hay, G., Castilla, G.: Geographic Object-Based Image Analysis (GEOBIA): A new name for a new discipline. In: Blaschke, T., Lang, S., Hay, G. (eds.) Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications, chap. 1.4, pp. 75–89. Springer, Heidelberg (2008)
Körting, T., Castejon, E., Fonseca, L.: Divide and Segment - An alternative for parallel segmentation. In: Proceedings of XII GeoINFO, pp. 97–104. INPE, Campos do Jordão (2011)
Lenkiewicz, P., Pereira, M., Freire, M., Fernandes, J.: A new 3D image segmentation method for parallel architectures. In: 2009 IEEE International Conference on Multimedia and Expo, pp. 1813–1816 (June 2009)
Meinel, G., Neubert, M.: A comparison of segmentation programs for high resolution remote sensing data. International Archives of Photogrammetry and Remote Sensing 35(Part B), 1097–1105 (2004)
Pinho, C., Silva, F., Fonseca, L., Monteiro, A.: Intra-urban land cover classification from high-resolution images using the C4.5 algorithm. ISPRS Congress Beijing 7 (2008)
Plaza, A., Plaza, J., Paz, A., Sanchez, S.: Parallel Hyperspectral Image and Signal Processing. IEEE Signal Processing Magazine 28, 119–126 (2011)
Prassni, J., Ropinski, T., Hinrichs, K.: Uncertainty-aware guided volume segmentation. IEEE Transactions on Visualization and Computer Graphics 16(6), 1358–1365 (2010)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 888–905 (2000)
Singh, D., Heras, D., Rivera, F.: Parallel Seeded Region Growing Algorithm. In: VIII Simposium Nacional de Reconocimiento de Formas y Análisis de Imágenes, Bilbao, Spain (1999)
Tan, P., Steinbach, M., Kumar, V., Potter, C., Klooster, S., Torregrosa, A.: Finding Spatio-Temporal Patterns in Earth Science Data. Earth Science, 1–12 (2001)
Valencia, D., Lastovetsky, A., O’Flynn, M., Plaza, A., Plaza, J.: Parallel Processing of Remotely Sensed Hyperspectral Images on Heterogeneous Networks of Workstations Using HeteroMPI. IJHPCA, 386–407 (November 2008)
Wassenberg, J., Middelmann, W., Sanders, P.: An efficient parallel algorithm for graph-based image segmentation. In: Jiang, X., Petkov, N. (eds.) CAIP 2009. LNCS, vol. 5702, pp. 1003–1010. Springer, Heidelberg (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Körting, T.S., Castejon, E.F., Fonseca, L.M.G. (2013). The Divide and Segment Method for Parallel Image Segmentation. In: Blanc-Talon, J., Kasinski, A., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2013. Lecture Notes in Computer Science, vol 8192. Springer, Cham. https://doi.org/10.1007/978-3-319-02895-8_45
Download citation
DOI: https://doi.org/10.1007/978-3-319-02895-8_45
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02894-1
Online ISBN: 978-3-319-02895-8
eBook Packages: Computer ScienceComputer Science (R0)