论文标题
FPGA：完全端到端高光谱图像分类的快速无补丁的全局学习框架
FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification
论文作者
论文摘要
深度学习技术为高光谱图像（HSI）分类提供了重大改进。当前基于深度学习的HSI分类器通过将图像分为重叠的补丁来遵循基于补丁的学习框架。因此，这些方法是本地学习方法，其计算成本很高。在本文中，提出了用于HSI分类的快速无补丁全球学习（FPGA）框架。在FPGA中，基于编码器的FCN通过处理整个图像来考虑全局空间信息，从而导致快速推断。但是，很难直接利用基于编码器的FCN进行HSI分类，因为由于有限的培训样本引起的不同梯度不足，因此它总是无法收敛。为了解决差异问题并保持快速推理和全局空间信息挖掘的FCN能力，首先提出了全球随机分层采样策略，是通过将所有训练样本转换为分层样本的随机序列来提出的。该策略可以获得不同的梯度，以确保FPGA框架中FCN的收敛性。为了更好地设计FCN体系结构，FreeNET是一个用于HSI分类的完全端到端网络，是为了最大程度地利用全球空间信息的利用，并通过基于频谱注意的编码器和轻量级解码器来提高性能。还设计了一个横向连接模块，以连接编码器和解码器，融合编码器中的空间详细信息以及解码器中的语义特征。使用三个公共基准数据集获得的实验结果表明，FPGA框架在HSI分类的速度和准确性方面都优于基于补丁的框架。代码已在以下网址提供：https：//github.com/z-zheng/freenet。
Deep learning techniques have provided significant improvements in hyperspectral image (HSI) classification. The current deep learning based HSI classifiers follow a patch-based learning framework by dividing the image into overlapping patches. As such, these methods are local learning methods, which have a high computational cost. In this paper, a fast patch-free global learning (FPGA) framework is proposed for HSI classification. In FPGA, an encoder-decoder based FCN is utilized to consider the global spatial information by processing the whole image, which results in fast inference. However, it is difficult to directly utilize the encoder-decoder based FCN for HSI classification as it always fails to converge due to the insufficiently diverse gradients caused by the limited training samples. To solve the divergence problem and maintain the abilities of FCN of fast inference and global spatial information mining, a global stochastic stratified sampling strategy is first proposed by transforming all the training samples into a stochastic sequence of stratified samples. This strategy can obtain diverse gradients to guarantee the convergence of the FCN in the FPGA framework. For a better design of FCN architecture, FreeNet, which is a fully end-to-end network for HSI classification, is proposed to maximize the exploitation of the global spatial information and boost the performance via a spectral attention based encoder and a lightweight decoder. A lateral connection module is also designed to connect the encoder and decoder, fusing the spatial details in the encoder and the semantic features in the decoder. The experimental results obtained using three public benchmark datasets suggest that the FPGA framework is superior to the patch-based framework in both speed and accuracy for HSI classification. Code has been made available at: https://github.com/Z-Zheng/FreeNet.
