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.
In the other study, REF proposed a deep active learning approach using Deep Belief Network (DBN) for classifying hyperspectral images in remote sensing application.
Active Deep Learning for Classification of Hyperspectral Images
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Mathematics Computer Science