Active Deep Learning for Classification of Hyperspectral Images
This work addresses the high cost of labeling in remote sensing applications, but it is incremental as it builds on existing active learning and deep learning techniques.
The paper tackles the problem of expensive labeling for hyperspectral image classification by proposing an active deep learning algorithm that selects training samples based on representativeness and uncertainty, showing it is efficient and effective compared to other methods.
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.