Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-8, 913-917, 2014
https://doi.org/10.5194/isprsarchives-XL-8-913-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
28 Nov 2014
Graph – Based High Resolution Satellite Image Segmentation for Object Recognition
K. Ravali1, M. V. Ravi Kumar2, and K. Venugopala Rao2 1Department of Computer Science & Engineering, Sreenidhi Institute of Science and Technology, Hyderabad, India
2National Remote Sensing Centre, ISRO, Department of Space, Balanagar, Hyderabad, India
Keywords: High Resolution, segmentation, classification, graph, computer vision, Mahalanobis Abstract. Object based image processing and analysis is challenging research in very high resolution satellite utilisation. Commonly ei ther pixel based classification or visual interpretation is used to recognize and delineate land cover categories. The pixel based classification techniques use rich spectral content of satellite images and fail to utilise spatial relations. To overcome th is drawback, traditional time consuming visual interpretation methods are being used operational ly for preparation of thematic maps. This paper addresses computational vision principles to object level image segmentation. In this study, computer vision algorithms are developed to define the boundary between two object regions and segmentation by representing image as graph. Image is represented as a graph G (V, E), where nodes belong to pixels and, edges (E) connect nodes belonging to neighbouring pixels. The transformed Mahalanobis distance has been used to define a weight function for partition of graph into components such that each component represents the region of land category. This implies that edges between two vertices in the same component have relatively low weights and edges between vertices in different components should have higher weights. The derived segments are categorised to different land cover using supervised classification.

The paper presents the experimental results on real world multi-spectral remote sensing images of different landscapes such as Urban, agriculture and mixed land cover. Graph construction done in C program and list the run time for both graph construction and segmentation calculation on dual core Intel i7 system with 16 GB RAM, running 64bit window 7.

Conference paper (PDF, 2042 KB)


Citation: Ravali, K., Ravi Kumar, M. V., and Venugopala Rao, K.: Graph – Based High Resolution Satellite Image Segmentation for Object Recognition, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-8, 913-917, https://doi.org/10.5194/isprsarchives-XL-8-913-2014, 2014.

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