Authors: Cheng Deng,Yumeng Xue,Xianglong Liu,Chao Li,Dacheng Tao
ArXiv: 1904.02454
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Abstract URL: http://arxiv.org/abs/1904.02454v1
Deep learning has recently attracted significant attention in the field of
hyperspectral images (HSIs) classification. However, the construction of an
efficient deep neural network (DNN) mostly relies on a large number of labeled
samples being available. To address this problem, this paper proposes a unified
deep network, combined with active transfer learning that can be well-trained
for HSIs classification using only minimally labeled training data. More
specifically, deep joint spectral-spatial feature is first extracted through
hierarchical stacked sparse autoencoder (SSAE) networks. Active transfer
learning is then exploited to transfer the pre-trained SSAE network and the
limited training samples from the source domain to the target domain, where the
SSAE network is subsequently fine-tuned using the limited labeled samples
selected from both source and target domain by corresponding active learning
strategies. The advantages of our proposed method are threefold: 1) the network
can be effectively trained using only limited labeled samples with the help of
novel active learning strategies; 2) the network is flexible and scalable
enough to function across various transfer situations, including cross-dataset
and intra-image; 3) the learned deep joint spectral-spatial feature
representation is more generic and robust than many joint spectral-spatial
feature representation. Extensive comparative evaluations demonstrate that our
proposed method significantly outperforms many state-of-the-art approaches,
including both traditional and deep network-based methods, on three popular
datasets.