Deep Intrinsic Decomposition with Adversarial Learning for Hyperspectral
  Image Classification
Zhiqiang Gong Xian Zhou Wen Yao
Convolutional neural networks (CNNs) have been demonstrated their powerful
ability to extract discriminative features for hyperspectral image
classification. However, general deep learning methods for CNNs ignore the
influence of complex environmental factor which enlarges the intra-class
variance and decreases the inter-class variance. This multiplies the difficulty
to extract discriminative features. To overcome this problem, this work
develops a novel deep intrinsic decomposition with adversarial learning, namely
AdverDecom, for hyperspectral image classification to mitigate the negative
impact of environmental factors on classification performance. First, we
develop a generative network for hyperspectral image (HyperNet) to extract the
environmental-related feature and category-related feature from the image.
Then, a discriminative network is constructed to distinguish different
environmental categories. Finally, a environmental and category joint learning
loss is developed for adversarial learning to make the deep model learn
discriminative features. Experiments are conducted over three commonly used
real-world datasets and the comparison results show the superiority of the
proposed method. The implementation of the proposed method and other compared
methods could be accessed at https://github.com/shendu-sw/Adversarial Learning
Intrinsic Decomposition for the sake of reproducibility.