Towards Fast Morphological Mosaicking of High-Resolution Multi-Spectral Products – On Improvements of Seamlines

Abstract. The complex process of fully automatically establishing seamlines for the fast production of high-quality mosaics with high-amount of high-resolution multi-spectral images is detailed and improved in this paper. The algorithm is analyzed and a quasi-linear runtime in the number of considered pixels is proven for all situations. For typical situations the storage is even essentially smaller from a complexity theoretical perspective. Improvements from algorithm practical perspective are specified, too. The influence of different methods for the determination of seamlines based on gradients is investigated in detail for three Sentinel-2 products. The studied techniques cover well-known ones normally based on a single band. But also more sophisticated techniques based on multiple bands or even taking additional external geo-information data are taken into account. Based on the results a larger area covered by Image2006 orthorectified products with data of the Resourcesat-1 mission is regarded. The feasibility of applying advanced subordinated methods for improving the mosaic such as radiometric harmonization is examined. This also illustrates the robustness of the improved seamline determination approaches.

[1]  Pierre Soille,et al.  Morphological image compositing , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  P. d'Angelo Radio metric alignment and vignetting calibration , 2007 .

[3]  Philippe Martimort,et al.  Sentinel-2 level 1 products and image processing performances , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[4]  Donald E. Knuth,et al.  The Art of Computer Programming: Volume 3: Sorting and Searching , 1998 .

[5]  Luc Vincent,et al.  Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  P. Reinartz,et al.  Automated Georeferencing of Optical Satellite Data with Integrated Sensor Model Improvement , 2012 .

[7]  William J. Volchok,et al.  Radiometric scene normalization using pseudoinvariant features , 1988 .

[8]  Hanno Scharr,et al.  Principles of Filter Design , 1999 .

[9]  Le Yu,et al.  Towards the automatic selection of optimal seam line locations when merging optical remote-sensing images , 2012 .

[10]  C. Panem,et al.  AUTOMATIC AND GENERIC MOSAICING OF MULTISENSOR IMAGES: AN APPLICATION TO PLEIADES HR , 2012 .

[11]  Robert Garfinkel,et al.  Mosaicking of Aerial Photographic Maps Via Seams Defined by Bottleneck Shortest Paths , 1998, Oper. Res..

[12]  Ingo Wegener,et al.  Complexity Theory , 2005 .

[13]  Igor Ogashawara,et al.  A Quantitative Approach for Analyzing the Relationship between Urban Heat Islands and Land Cover , 2012, Remote. Sens..

[14]  D. Roy,et al.  Web-enabled Landsat Data (WELD): Landsat ETM+ composited mosaics of the conterminous United States , 2010 .

[15]  J. Cihlar Land cover mapping of large areas from satellites: Status and research priorities , 2000 .

[16]  Youchuan Wan,et al.  Automatic determination of seamlines for aerial image mosaicking based on vector roads alone , 2013 .

[17]  Aime Meygret,et al.  SENTINEL-2 LEVEL 1 PRODUCTS AND IMAGE PROCESSING PERFORMANCES , 2012 .

[18]  Donald E. Knuth,et al.  The Art of Computer Programming, Vol. 3: Sorting and Searching , 1974 .