Xia Kunming, Yuan Guowu, Xia Mengen, Li Xiaosen, Gui Jinkang, Zhou Hao
School of Information Science and Engineering, Yunnan University, Kunming 650504, China.
Sensors (Basel). 2024 Aug 21;24(16):5386. doi: 10.3390/s24165386.
With the advancement of deep learning, related networks have shown strong performance for Hyperspectral Image (HSI) classification. However, these methods face two main challenges in HSI classification: (1) the inability to capture global information of HSI due to the restriction of patch input and (2) insufficient utilization of information from limited labeled samples. To overcome these challenges, we propose an Advanced Global Prototypical Segmentation (AGPS) framework. Within the AGPS framework, we design a patch-free feature extractor segmentation network (SegNet) based on a fully convolutional network (FCN), which processes the entire HSI to capture global information. To enrich the global information extracted by SegNet, we propose a Fusion of Lateral Connection (FLC) structure that fuses the low-level detailed features of the encoder output with the high-level features of the decoder output. Additionally, we propose an Atrous Spatial Pyramid Pooling-Position Attention (ASPP-PA) module to capture multi-scale spatial positional information. Finally, to explore more valuable information from limited labeled samples, we propose an advanced global prototypical representation learning strategy. Building upon the dual constraints of the global prototypical representation learning strategy, we introduce supervised contrastive learning (CL), which optimizes our network with three different constraints. The experimental results of three public datasets demonstrate that our method outperforms the existing state-of-the-art methods.
随着深度学习的发展，相关网络在高光谱图像（HSI）分类方面展现出了强大的性能。然而，这些方法在HSI分类中面临两个主要挑战：（1）由于补丁输入的限制，无法捕捉HSI的全局信息；（2）对有限标记样本的信息利用不足。为了克服这些挑战，我们提出了一种先进的全局原型分割（AGPS）框架。在AGPS框架内，我们基于全卷积网络（FCN）设计了一个无补丁特征提取器分割网络（SegNet），该网络处理整个HSI以捕捉全局信息。为了丰富SegNet提取的全局信息，我们提出了一种横向连接融合（FLC）结构，将编码器输出的低级详细特征与解码器输出的高级特征进行融合。此外，我们提出了一种空洞空间金字塔池化-位置注意力（ASPP-PA）模块来捕捉多尺度空间位置信息。最后，为了从有限的标记样本中探索更多有价值的信息，我们提出了一种先进的全局原型表示学习策略。基于全局原型表示学习策略的双重约束，我们引入了监督对比学习（CL），它通过三种不同的约束来优化我们的网络。三个公共数据集的实验结果表明，我们的方法优于现有的最先进方法。
