Bai Yang, Sun Xiyan, Ji Yuanfa, Fu Wentao, Duan Xiaoyu
Information and Communicaiton Schnool, Guilin University of Electronic Technology, Guilin 541004, China.
Guangxi Key Laboratory of Precision Navigation Technology and Application, Guilin University of Electronic Technology, Guilin 541004, China.
Sensors (Basel). 2023 Oct 22;23(20):8635. doi: 10.3390/s23208635.
The lack of labeled training samples restricts the improvement of Hyperspectral Remote Sensing Image (HRSI) classification accuracy based on deep learning methods. In order to improve the HRSI classification accuracy when there are few training samples, a Lightweight 3D Dense Autoencoder Network (L3DDAN) is proposed. Structurally, the L3DDAN is designed as a stacked autoencoder which consists of an encoder and a decoder. The encoder is a hybrid combination of 3D convolutional operations and 3D dense block for extracting deep features from raw data. The decoder composed of 3D deconvolution operations is designed to reconstruct data. The L3DDAN is trained by unsupervised learning without labeled samples and supervised learning with a small number of labeled samples, successively. The network composed of the fine-tuned encoder and trained classifier is used for classification tasks. The extensive comparative experiments on three benchmark HRSI datasets demonstrate that the proposed framework with fewer trainable parameters can maintain superior performance to the other eight state-of-the-art algorithms when there are only a few training samples. The proposed L3DDAN can be applied to HRSI classification tasks, such as vegetation classification. Future work mainly focuses on training time reduction and applications on more real-world datasets.
标记训练样本的缺乏限制了基于深度学习方法的高光谱遥感图像（HRSI）分类精度的提高。为了在训练样本较少的情况下提高HRSI分类精度，提出了一种轻量级3D密集自动编码器网络（L3DDAN）。在结构上，L3DDAN被设计为一个堆叠自动编码器，由一个编码器和解码器组成。编码器是3D卷积操作和3D密集块的混合组合，用于从原始数据中提取深度特征。由3D反卷积操作组成的解码器用于重建数据。L3DDAN先后通过无监督学习（无标记样本）和有监督学习（少量标记样本）进行训练。由微调后的编码器和训练好的分类器组成的网络用于分类任务。在三个基准HRSI数据集上进行的广泛对比实验表明，当只有少量训练样本时，所提出的框架具有较少的可训练参数，能够保持优于其他八种先进算法的性能。所提出的L3DDAN可应用于HRSI分类任务，如植被分类。未来的工作主要集中在减少训练时间以及在更多真实世界数据集上的应用。
