STRUCTURAL HIGH-RESOLUTION SATELLITE IMAGE INDEXING

Satellite images with high spatial resolution raise many challenging issues in image understanding and pattern recognition. First, they allow measurement of small objects maybe up to 0.5 m, and both texture and geometrical structures emerge simultaneously. Second, objects in the same type of scenes might appear at different scales and orientations. Consequently, image indexing methods should combine the structure and texture information of images and comply with some invariant properties. This paper contributes to the indexing of high-resolution satellite images. We suggest a satellite image indexing method relying on topographic maps (Caselles et al., 1999) and a shape-based image indexing scheme (Xia et al., 2009). The proposed approach contains both the textural and structural information of satellite images and is also robust to changes in scale, orientation and contrast. Experimental analysis on a real satellite image database confirms the efficiency of the approach.

[1]  Chung-Sheng Li,et al.  Deriving texture feature set for content-based retrieval of satellite image database , 1997, Proceedings of International Conference on Image Processing.

[2]  John A. Richards,et al.  Remote Sensing Digital Image Analysis: An Introduction , 1999 .

[3]  Pascal Monasse,et al.  Fast computation of a contrast-invariant image representation , 2000, IEEE Trans. Image Process..

[4]  L. Ruiz,et al.  TEXTURE FEATURE EXTRACTION FOR CLASSIFICATION OF REMOTE SENSING DATA USING WAVELET DECOMPOSITION : A COMPARATIVE STUDY , 2004 .

[5]  Song-Chun Zhu,et al.  What are Textons? , 2005, International Journal of Computer Vision.

[6]  Jean-Michel Morel,et al.  Topographic Maps and Local Contrast Changes in Natural Images , 1999, International Journal of Computer Vision.

[7]  Kim L. Boyer,et al.  Classifying land development in high-resolution panchromatic satellite images using straight-line statistics , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Jean-Michel Morel,et al.  Geometry and Color in Natural Images , 2002, Journal of Mathematical Imaging and Vision.

[9]  Shawn D. Newsam,et al.  Comparing global and interest point descriptors for similarity retrieval in remote sensed imagery , 2007, GIS.

[10]  Josiane Zerubia,et al.  Computing Statistics from Man-Made Structures on the Earth's Surface for Indexing Satellite Images , 2007 .

[11]  Henri Maître,et al.  Semantic Annotation of Satellite Images , 2007, MLDM Posters.

[12]  Avik Bhattacharya,et al.  Indexing of mid-resolution satellite images with structural attributes. , 2008 .

[13]  Julie Delon,et al.  Shape-based Invariant Texture Indexing , 2010, International Journal of Computer Vision.