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Impact of Spectral Domain Features for Small Object Detection in Remote Sensing

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Computer, Communication, and Signal Processing. AI, Knowledge Engineering and IoT for Smart Systems (ICCCSP 2023)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 670))

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Abstract

Remote sensing is a vital technique for detecting and monitoring the physical characteristics of an area from a distance: typically, from an aircraft or satellite. Accurate object detection in remote sensing images is crucial since security, transportation, environmental monitoring, and rescue applications in the military and civilian sectors demand comprehensive, precise analysis and utilization of these images. Despite recent advances, small object detection in satellite images is challenging due to the lack of information raised from the area covered by small objects in the image. To address the problem of small object detection, this paper discusses the importance of feature space analysis. The need for distinctive feature space is equally important along with the choice of the model architecture. Focusing on feature space analysis, deep learning is used for the extraction of the granularity of data. Further, spectral context is combined with spatial features to reinforce feature information and thus, enhance the detection. Our strategy involves comparing two units: the Fourier transform unit and the Wavelet packet transform unit to extract spectral features. These units are placed in the EfficientDet to enhance the feature space and assist small object detection by providing context. Three models were trained and tested on DIOR dataset, which is a publicly available dataset. The intuition of adding the transform units to the existing model has shown a remarkable improvement of 20% in the mAP. With limited changes in the number of parameters and without much hyper parameter tuning, the mAP shows improvement. Initially an experiment was conducted on a small custom dataset to test the hypothesis of the impact of feature space in object detection and the models containing the transform units showed an improvement of 4% in the mAP.

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Authors and Affiliations

  1. Ramaiah Institute of Technology, Bengaluru, India

    Urja Giridharan

  2. Department of Aerospace, IISc, Bengaluru, India

    Neeraj Ramprasad, Sukanta Roy & S. N. Omkar

Authors
  1. Urja Giridharan
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  2. Neeraj Ramprasad
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  3. Sukanta Roy
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  4. S. N. Omkar
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Corresponding author

Correspondence to S. N. Omkar .

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Editors and Affiliations

  1. University of Reims Champagne-Ardenne, Paris, France

    Eunika Mercier-Laurent

  2. Toronto Metropolitan University, Toronto, Canada

    Xavier Fernando

  3. Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, India

    Aravindan Chandrabose

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Giridharan, U., Ramprasad, N., Roy, S., Omkar, S.N. (2023). Impact of Spectral Domain Features for Small Object Detection in Remote Sensing. In: Mercier-Laurent, E., Fernando, X., Chandrabose, A. (eds) Computer, Communication, and Signal Processing. AI, Knowledge Engineering and IoT for Smart Systems. ICCCSP 2023. IFIP Advances in Information and Communication Technology, vol 670. Springer, Cham. https://doi.org/10.1007/978-3-031-39811-7_15

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  • DOI: https://doi.org/10.1007/978-3-031-39811-7_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-39810-0

  • Online ISBN: 978-3-031-39811-7

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