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Searching and describing objects in satellite images on the basis of modeling reasoning
D.R. Kasimov 1

1 Kalashnikov Izhevsk State Technical University,
426069, Izhevsk, Russia, Studencheskaya 7

 PDF, 2034 kB

DOI: 10.18287/2412-6179-CO-716

Pages: 772-781.

Full text of article: Russian language.

Abstract:
The article presents an approach to a problem of contextual search and description of objects in raster satellite images, which consists in modeling reasoning on the basis of structured cases. As a result of image processing, an adjacency graph of color regions is constructed. The object is characterized by color, attributes of the form of segments of the border and the shape of the object as a whole. A structured case is represented in the form of a beam graph, whose arcs are ordered according to a positive bypass of the region boundaries. Using a graph matching algorithm, occurrences of cases stored in the system database are detected in the analyzed image. When the occurrence is detected, a case-based inference rule is applied. The degree to which an object belongs to a certain class depends not only on the properties of the object itself, but also on the reliability of the surrounding objects. The contextual search strategy contains stages of recursion and iteration. In contrast to neural network technologies, the proposed approach allows one not only to classify image objects, but also to form their structured descriptions. In addition, the classification decision issued by the system has a reasoned justification. The results of the experiment show that reasoning based on structured cases allows refining the results of classification and increasing the reliability of object recognition in satellite images.

Keywords:
computer vision, digital image processing, pattern recognition, structural analysis, segmentation, approximation, adjacency graph, beam graph, case-based reasoning.

Citation:
Kasimov DR. Searching and describing objects in satellite images on the base of modeling reasoning. Computer Optics 2020; 44(5): 772-781. DOI: 10.18287/2412-6179-CO-716.

Acknowledgements:
The research was financially supported by the Russian Science Foundation (Project No. 18-71-00109).

References:
  1. Maggiori E, Tarabalka Y, Charpiat G, Alliez P. Can semantic labeling methods generalize to any city? The Inria Aerial Image Labeling Benchmark. IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2017: 3226-3229.
  2. Hamaguchi R, Hikosaka S. Building Detection From Satellite Imagery Using Ensemble of Size-Specific Detectors. IEEE Conf on Computer Vision and Pattern Recognition (CVPR) Workshops 2018; 187-191.
  3. Zhang A, Liu X, Gros A, Tiecke T. Building Detection from Satellite Images on a Global Scale. 30th Conference on Neural Information Processing Systems (NIPS 2016) 2017. Source: <https://arxiv.org/abs/1707.08952>.
  4. Badrinarayanan V, Kendall A. SegNet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 2017; 39(12): 2481-2495.
  5. Filin O, Zapara A, Panchenko S. Road detection with EOSResUNet and post vectorizing algorithm. IEEE Conf on Computer Vision and Pattern Recognition (CVPR) Workshops 2018; 211-215.
  6. Hamid R, O'Hara S, Tabb M. Global-scale object detection using satellite imagery. ISPRS Archives 2014; XL-3: 107-113.
  7. Huang X, Zhang LP. A multidirectional and multiscale morphological index for automatic building extraction from multispectral GeoEye-1 imagery. Photogramm Eng Remote Sensing 2011; 77(7): 721-732.
  8. Zhang Q, Huang X, Zhang GX. A morphological building detection framework for high-resolution optical imagery over urban areas. IEEE Geosci Remote Sens Lett 2016; 13: 1388-1392.
  9. You Y, Wang S, Ma Y, Chen G, Wang B, Shen M, Liu W. Building detection from VHR remote sensing imagery based on the morphological building index. Remote Sens 2018; 10(8), 1288.
  10. Gurevich IB, Yashina VV. Descriptive Image Analysis: Genesis and Current Trends. Pattern Recognition and Image Analysis 2017; 27(4): 653-674.
  11. Asatryan DG. Gradient-based technique for image structural analysis and applications. Computer Optics 2019; 43(2): 245-250. DOI: 10.18287/2412-6179-2019-43-2-245-250.
  12. Krasnabayeu YA, Chistabayeu DV, Malyshev AL. Comparison of binary feature points descriptors of images under distortion conditions. Computer Optics 2019; 43(3): 434-445. 10.18287/2412-6179-2019-43-3-434-445.
  13. Fu KS. Syntactic methods in pattern recognition. New York, London: Academic Press; 1974.
  14. Kasimov DR, Kuchuganov AV, Kuchuganov VN, Oskolkov PP. Approximation of color images based on the clusterization of the color palette and smoothing boundaries by splines and arcs. Program. Comput Softw 2018; 44(5): 295-302.
  15. Zadeh LA. The concept of a linguistic variable and its application to approximate reasoning—I. Inf Sci 1975; 8(3): 199-249.
  16. Kuchuganov AV. Recursions in image analysis problems. Pattern Recognit Image Anal 2009; 19(3): 501-507.
  17. Baader F, Borgwardt S, Peñaloza R. Decidability and complexity of fuzzy description logics. Künstliche Intelligenz 2017; 31(1): 85-90.
  18. Yan J, Yin XC, Lin W, Deng C, Zha H, Yang X. A short survey of recent advances in graph matching. Proc 2016 ACM Int Conf on Multimedia Retrieval 2016; 167-174.
  19. Rajput MK, Kamalapur S. A survey on subgraph matching algorithm for graph database. International Journal for Scientific Research & Development 2016; 3(12): 149-152.
  20. Fernandez-Moral E, Martins R, Wolf D, Rives P. A new metric for evaluating semantic segmentation: leveraging global and contour accuracy. Workshop on Planning, Perception and Navigation for Intelligent Vehicles (PPNIV17) 2018. 1051-1056.
  21. Blaschke T, Hay GJ, Kelly M, Lang S, Hofmann P, Addink E, Feitosa RQ, Meer F, Werff H, Coillie F, Tiede D. Geographic object-based image analysis – Towards a new paradigm. ISPRS J Photogramm Remote Sens 2014; 87: 180-191.

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