Deep Intrinsic Decomposition with Adversarial Learning for Hyperspectral Image Classification
This work addresses hyperspectral image classification for remote sensing applications, but it is incremental as it builds on existing CNN approaches by incorporating adversarial learning to handle environmental variations.
The paper tackles the problem of hyperspectral image classification being hindered by environmental factors that increase intra-class variance and reduce inter-class variance, by proposing AdverDecom, a deep intrinsic decomposition method with adversarial learning, which shows superiority over existing methods on three real-world datasets.
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.