{
  "id": "2012.09284",
  "version": "v1",
  "published": "2020-12-16T21:46:33.000Z",
  "updated": "2020-12-16T21:46:33.000Z",
  "title": "Sparse Signal Models for Data Augmentation in Deep Learning ATR",
  "authors": [
    "Tushar Agarwal",
    "Nithin Sugavanam",
    "Emre Ertin"
  ],
  "comment": "12 pages, 5 figures, to be submitted to IEEE Transactions on Geoscience and Remote Sensing",
  "categories": [
    "cs.CV",
    "cs.LG",
    "eess.IV",
    "eess.SP"
  ],
  "abstract": "Automatic Target Recognition (ATR) algorithms classify a given Synthetic Aperture Radar (SAR) image into one of the known target classes using a set of training images available for each class. Recently, learning methods have shown to achieve state-of-the-art classification accuracy if abundant training data is available, sampled uniformly over the classes, and their poses. In this paper, we consider the task of ATR with a limited set of training images. We propose a data augmentation approach to incorporate domain knowledge and improve the generalization power of a data-intensive learning algorithm, such as a Convolutional neural network (CNN). The proposed data augmentation method employs a limited persistence sparse modeling approach, capitalizing on commonly observed characteristics of wide-angle synthetic aperture radar (SAR) imagery. Specifically, we exploit the sparsity of the scattering centers in the spatial domain and the smoothly-varying structure of the scattering coefficients in the azimuthal domain to solve the ill-posed problem of over-parametrized model fitting. Using this estimated model, we synthesize new images at poses and sub-pixel translations not available in the given data to augment CNN's training data. The experimental results show that for the training data starved region, the proposed method provides a significant gain in the resulting ATR algorithm's generalization performance.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-12-16T21:46:33.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "data augmentation",
      "sparse signal models",
      "deep learning atr",
      "persistence sparse modeling approach",
      "synthetic aperture radar"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 12,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}