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Detecting aircrafts from satellite images using saliency and conical pyramid based template representation

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Abstract

Automatic target localization in satellite images still remains as a challenging problem in the field of computer vision. The issues involved in locating targets in satellite images are viewpoint, spectral (intensity) and scale variations. Diversity in background texture and target clutter also adds up to the complexity of the problem of localizing aircrafts in satellite images. Failure of modern feature extraction and object detection methods highlight the complexity of the problem. In the proposed work, pre-processing techniques, viz. denoising and contrast enhancement, are first used to improve the quality of the images. Then, the concept of unsupervised saliency is used to detect the potential regions of interest, which reduces the search space. Parts from the salient regions are further processed using clustering and morphological processing to get the probable regions of isolated aircraft targets. Finally, a novel conical pyramid based framework for template representation of the target samples is proposed for matching. Experimental results shown on a few satellite images exhibit the superior performance of the proposed methods.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology Madras, Chennai, 600036, India

    Samik Banerjee, Nitin Gupta & Sukhendu Das

  2. Defence Terrain Research Laboratory, Defence Research and Development Organization, Delhi, 110054, India

    Pinaki Roy Chowdhury & L K Sinha

Authors
  1. Samik Banerjee
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  2. Nitin Gupta
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  3. Sukhendu Das
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  4. Pinaki Roy Chowdhury
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  5. L K Sinha
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Correspondence to Sukhendu Das.

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Banerjee, S., Gupta, N., Das, S. et al. Detecting aircrafts from satellite images using saliency and conical pyramid based template representation. Sādhanā 41, 1155–1171 (2016). https://doi.org/10.1007/s12046-016-0540-5

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  • DOI: https://doi.org/10.1007/s12046-016-0540-5

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