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Hyperspectral image classification based on optimized convolutional neural networks with 3D stacked blocks
Zhang, Xiaoxia1; Guo, Yong2; Zhang, Xia1
2022-01-11
摘要3D convolution can fully utilize the spectral-spatial characteristics of hyperspectral image (HSI), and stacked blocks with deep layers are capable of extracting hidden features and utilizing discriminant information for classification. Naturally, a 3D convolutional neural network (CNN) based on stacked blocks named SB-3D-CNN is presented for HSI classification. Moreover, the proposed network introduces the attention mechanism before the fully connected layer, which can filter out interfering information effectively. Then we optimized the architecture to obtain optimal results on three commonly used datasets of Indian Pines, Salinas and Pavia University. Experimental results demonstrate that the optimized architecture achieves better classification rates than related recent works. Because the classification accuracies on the three datasets have reached saturation, we transferred the optimized architecture to a more complex dataset adopting the airborne hyperspectral data, which obtains from Guangxi province in south China. The results show that the optimized architecture achieves superior classification accuracies compared with other state-of-the-art methods. These results also demonstrate the optimized SB-3D-CNN has the advantages of validity and portability to more complex data.
关键词Convolutional neural network 3D convolution Hyperspectral image classification Stacked blocks Attention mechanism Spectral-spatial characteristics
DOI10.1007/s12145-021-00731-1
发表期刊EARTH SCIENCE INFORMATICS
ISSN1865-0473
页码13
通讯作者Zhang, Xiaoxia(2017010117@stu.cdut.edu.cn)
收录类别SCI
WOS记录号WOS:000741604200001
语种英语