计算机科学 ›› 2020, Vol. 47 ›› Issue (6): 151-156.doi: 10.11896/jsjkx.190500147
王燕, 王丽
WANG Yan, WANG Li
摘要: 针对高光谱图像特征利用不足的问题,提出了一种新的基于空谱联合特征的高光谱图像分类方法。该方法首先利用主成分分析(Principal Component Analysis,PCA)和线性判别分析(Linear Discriminant Analysis,LDA)对高光谱图像进行组合降维;其次引入Gabor核,设计了一种基于Gabor核的卷积(Local Gabor Convolutional,LGC)层;最后基于LGC层设计了一个新的卷积神经网络(Local Gabor Convolutional Neural Network,LGCNN)进行分类。在Indian Pines和Salinas Scene数据集上对所提方法进行验证,并将其与其他经典分类方法进行比较。实验结果表明,该方法不仅能大幅度减少可学习的参数,降低模型复杂度,而且具备较好的分类性能,其总体精度达到99%,平均分类精度达到98%以上,Kappa系数达到98%以上。
中图分类号:
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