Active Deep Learning for Classification of Hyperspectral Images

Active deep learning classification of hyperspectral images is considered in this paper. Deep learning has achieved success in many applications, but good-quality labeled samples are needed to construct a deep learning network. It is expensive getting good labeled samples in hyperspectral images for remote sensing applications. An active learning algorithm based on a weighted incremental dictionary learning is proposed for such applications. The proposed algorithm selects training samples that maximize two selection criteria, namely representative and uncertainty. This algorithm trains a deep network efficiently by actively selecting training samples at each iteration. The proposed algorithm is applied for the classification of hyperspectral images, and compared with other classification algorithms employing active learning. It is shown that the proposed algorithm is efficient and effective in classifying hyperspectral images.

• Publication year

  2016

• Venue

  IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

• Publication date

  2016-11-30

• Fields of study

  Computer Science, Environmental Science

• Identifiers
  DOI 10.1109/JSTARS.2016.2598859arXiv 1611.10031
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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