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- Title
基于CNN 的双边融合网络在高光谱图像分类中的应用.
- Authors
高红民; 曹雪莹; 杨耀; 花再军; 李臣明
- Abstract
Aiming at the issues of decreasing spatial resolution and feature loss caused by pooling operation in depth CNN-based hyperspectral image classification algorithm, a bilateral fusion block network (DFBN)composed of bilateral fusion blocks was designed. The upper structure of bilateral fusion block was constituted by 1×1 convolution and hyperlink, which was used to transfer local spatial characteristics. The lower structure was constituted by pooling layer, convolutional layer, deconvolution layer and upsampling to enhance the characteristics of efficient discrimination. Experimental results on three benchmark hyperspectral image data sets illustrate that the model is superior to other similar classification models.
- Publication
Journal on Communication / Tongxin Xuebao, 2020, Vol 41, Issue 11, p132
- ISSN
1000-436X
- Publication type
Article
- DOI
10.11959/j.issn.1000-436x.2020238