面向高光谱影像分类的显著性特征提取方法

针对高光谱影像分类问题,提出了一种显著性特征提取方法。首先,利用超像素分割算法将高光谱影像3个相邻波段分割为若干个小区域。然后,基于分割得到的小区域计算反映不同区域的显著性特征。最后,沿着光谱方向采用大小为3、步长为1的滑窗法获得所有波段的显著性特征。进一步将提取的显著性特征与光谱特征进行结合,并将结合后的特征输入到支持向量机中进行分类。利用Pavia大学、Indian Pines和Salinas 3组高光谱影像数据进行分类试验。试验结果表明,与传统的空间特征提取方法和基于卷积神经网络的高光谱影像分类方法相比,提取的显著性特征能够获得更高的高光谱影像分类精度,且结合光谱特征能够进一步提高分类精度。

Salient feature extraction method for hyperspectral image classification

Aiming at the problem of hyperspectral image classification, a salient feature extraction method is proposed. Firstly, the method uses a superpixel segmentation algorithm to divide three adjacent bands of hyperspectral image into several small regions. Then, the salient features of different regions are calculated based on the small regions. Finally, the sliding window method with a size of 3 steps is used along the spectral direction to obtain the salient features of all bands. The extracted saliency features are further combined with the spectral features, and the combined features are fed into a support vector machine for classification. The classification experiments were carried out on three hyperspectral image datasets including Pavia University, Indian Pines and Salinas. The experimental results show that compared with the traditional spatial feature extraction method and the convolutional neural network based methods, the extracted salient features can obtain higher classification accuracy. Combining salient features and spectral features can further improve classification accuracy.