双分支多维注意特征融合的高光谱图像分类

Hyperspectral Image Classification Based on Double Branch Multidimensional Attention Feature Fusion

为改善高光谱图像小样本类别的分类性能,提高模型特征表达的稳健性,提出了双分支多维注意力特征融合的神经网络分类模型(DBMD)。DBMD采用两个分支分别进行光谱特征提取和混合特征提取。光谱分支通过密集连接的扩张卷积逐级提取特征,然后融合低、中、高级语义信息作为特征输出。混合分支采用3D-2D网络架构,并通过改进的Inception块提取空间尺度特征。此外,注意力机制分别应用于光谱、空间和空谱特征,进行特征细化,增强重要区域的特征响应。最后,将不同维度的细化特征联合输入至分类器进行分类。在Indian Pines和Salinas Valley数据集上利用5%和1%的样本进行实验,分别取得了98.40%和99.78%的总体精度,与其他六种网络架构相比,该模型在准确性和稳定性上都具有更优的表现。

To improve the classification performance of small sample classes of hyperspectral images and to enhance the robustness of the model feature representation,a neural network classification model with two-branch multidimensional attentional feature fusion(DBMD) is proposed.DBMD uses two branches for spectral feature extraction and hybrid feature extraction respectively.The spectral branch extracts features step-by-step through densely connected dilated convolution,and then fuses low,medium and high level semantic information as the feature output.The hybrid branch uses a 3D-2D network architecture and extracts spatial scale features through improved Inception blocks.In addition,the attention mechanism is applied to spectral,spatial and spatial-spectral feature extraction respectively for feature refinement and to enhance the feature response in important regions.Finally,the refined features of different dimensions are jointly input to the classifier for classification.Experiments using 5% and 1% samples on the Indian Pines and Salinas Valley datasets achieve an overall accuracy of 98.40% and 99.78% respectively,and the proposed model performs better in terms of accuracy and stability compared to the other six network architectures.