A Convolutional Neural Network with Fletcher-Reeves Algorithm for Hyperspectral Image Classification | |
Chen, Chen1; Ma, Yi1,2; Ren, Guangbo2 | |
刊名 | REMOTE SENSING |
2019-06 | |
卷号 | 11期号:11 |
关键词 | convolutional neural network (CNN) Fletcher-Reeves algorithm (F-R) conjugate gradient coastal wetland classification hyperspectral imagery |
ISSN号 | 2072-4292 |
DOI | 10.3390/rs11111325 |
英文摘要 | Deep learning models, especially the convolutional neural networks (CNNs), are very active in hyperspectral remote sensing image classification. In order to better apply the CNN model to hyperspectral classification, we propose a CNN model based on Fletcher-Reeves algorithm (F-R CNN), which uses the Fletcher-Reeves (F-R) algorithm for gradient updating to optimize the convergence performance of the model in classification. In view of the fact that there are fewer optional training samples in practical applications, we further propose a method of increasing the number of samples by adding a certain degree of perturbed samples, which can also test the anti-interference ability of classification methods. Furthermore, we analyze the anti-interference and convergence performance of the proposed model in terms of different training sample data sets, different batch training sample numbers and iteration time. In this paper, we describe the experimental process in detail and comprehensively evaluate the proposed model based on the classification of CHRIS hyperspectral imagery covering coastal wetlands, and further evaluate it on a commonly used hyperspectral image benchmark dataset. The experimental results show that the accuracy of the two models after increasing training samples and adjusting the number of batch training samples is improved. When the number of batch training samples is continuously increased to 350, the classification accuracy of the proposed method can still be maintained above 80.7%, which is 2.9% higher than the traditional one. And its time consumption is less than that of the traditional one while ensuring classification accuracy. It can be concluded that the proposed method has anti-interference ability and outperforms the traditional CNN in terms of batch computing adaptability and convergence speed. |
资助项目 | National Natural Science Foundation of China[61890964] ; National Natural Science Foundation of China[41206172] ; National Natural Science Foundation of China[41706209] |
WOS关键词 | SPECTRAL-SPATIAL CLASSIFICATION |
WOS研究方向 | Remote Sensing |
语种 | 英语 |
出版者 | MDPI |
WOS记录号 | WOS:000472648000068 |
内容类型 | 期刊论文 |
源URL | [http://ir.fio.com.cn:8080/handle/2SI8HI0U/24616] |
专题 | 自然资源部第一海洋研究所 |
通讯作者 | Ma, Yi |
作者单位 | 1.Shandong Univ Sci & Technol, Coll Geomat, Qingdao 266590, Shandong, Peoples R China 2.Minist Nat Resources, Inst Oceanog 1, Qingdao 266061, Shandong, Peoples R China |
推荐引用方式 GB/T 7714 | Chen, Chen,Ma, Yi,Ren, Guangbo. A Convolutional Neural Network with Fletcher-Reeves Algorithm for Hyperspectral Image Classification[J]. REMOTE SENSING,2019,11(11). |
APA | Chen, Chen,Ma, Yi,&Ren, Guangbo.(2019).A Convolutional Neural Network with Fletcher-Reeves Algorithm for Hyperspectral Image Classification.REMOTE SENSING,11(11). |
MLA | Chen, Chen,et al."A Convolutional Neural Network with Fletcher-Reeves Algorithm for Hyperspectral Image Classification".REMOTE SENSING 11.11(2019). |
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