论文标题
高光谱图像分类的主动深度连接的卷积网络
Active Deep Densely Connected Convolutional Network for Hyperspectral Image Classification
论文作者
论文摘要
在过去的几年中,基于深度学习的方法在高光谱图像分类中广受欢迎。但是,深度学习的成功归因于众多标记的样本。仅使用几个标记的样本来训练深度学习模型以达到高分类的准确性仍然非常具有挑战性。因此,本文提出了一个以端到端方式训练的积极的深度学习框架,以最大程度地减少高光谱图像分类成本。首先,为高光谱图像分类考虑了深度连接的卷积网络。与传统的主动学习方法不同,添加了一个额外的网络,将其添加到设计深连接的卷积网络中,以预测输入样本的丢失。然后,可以使用其他网络来暗示未标记的样本,即深连接的卷积网络更有可能产生错误的标签。请注意,附加网络使用深度连接的卷积网络的中间特征作为输入。因此,提出的方法是端到端框架。随后,将一些选定的样品手动标记并添加到训练样本中。因此,使用新的训练集对深度连接的卷积网络进行了培训。最后,重复上述步骤以迭代训练整个框架。广泛的实验表明,仅选择几个样本后,提出的方法可以达到分类的高度精度。
Deep learning based methods have seen a massive rise in popularity for hyperspectral image classification over the past few years. However, the success of deep learning is attributed greatly to numerous labeled samples. It is still very challenging to use only a few labeled samples to train deep learning models to reach a high classification accuracy. An active deep-learning framework trained by an end-to-end manner is, therefore, proposed by this paper in order to minimize the hyperspectral image classification costs. First, a deep densely connected convolutional network is considered for hyperspectral image classification. Different from the traditional active learning methods, an additional network is added to the designed deep densely connected convolutional network to predict the loss of input samples. Then, the additional network could be used to suggest unlabeled samples that the deep densely connected convolutional network is more likely to produce a wrong label. Note that the additional network uses the intermediate features of the deep densely connected convolutional network as input. Therefore, the proposed method is an end-to-end framework. Subsequently, a few of the selected samples are labelled manually and added to the training samples. The deep densely connected convolutional network is therefore trained using the new training set. Finally, the steps above are repeated to train the whole framework iteratively. Extensive experiments illustrates that the method proposed could reach a high accuracy in classification after selecting just a few samples.