基于3D卷积联合注意力机制的高光谱图像分类

由于高光谱图像存在较高的数据维数,会给分类过程带来一些困难。为了提高分类的准确率,提出了一种使用3D卷积联合注意力机制的高光谱图像分类方法。首先,将中心像素与周围相邻的其它像素进行配对,可以通过配对构成多组新的像素对,充分利用了像素之间的邻域相关性。接着,将像素对放入3D卷积联合注意力机制网络框架中进行分类,它能够对高光谱图像中的特征进行选择性的学习。最后,通过投票策略获得像素标签。实验是在两个真实的高光谱图像数据集上进行。结果表明,所提出的方法充分挖掘了高光谱图像的光谱空间特征,能有效地提高分类精度。

Hyperspectral Image Classification Based on 3D Convolution Joint Attention Mechanism

The high data dimension of hyperspectral images causes difficulties in the classification process.To improve the accuracy of classification,a hyperspectral image classification method using a 3D convolution joint attention mechanism is proposed.First,by pairing the center pixel with other pixels adjacent to it,it can form multiple sets of new pixel-pairs,and the neighborhood correlation between the pixels can be fully utilized.Then,the pixel-pairs are classified into the 3D convolution joint attention mechanism network framework,which can selectively learn the features in the hyperspectral image.Finally,the pixel label is obtained through the voting strategy.An experiment was carried out on two real hyperspectral image datasets.The results show that the proposed method fully exploits the spectral–spatial features of hyperspectral images,and this can effectively improve the classification accuracy.