A Convolutional Neural Network with Fletcher–Reeves Algorithm for Hyperspectral Image Classification
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...
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doaj-6bf2a1ea7f974191b9d8926f32f701862020-11-25T01:09:21ZengMDPI AGRemote Sensing2072-42922019-06-011111132510.3390/rs11111325rs11111325A Convolutional Neural Network with Fletcher–Reeves Algorithm for Hyperspectral Image ClassificationChen Chen0Yi Ma1Guangbo Ren2College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Geomatics, Shandong University of Science and Technology, Qingdao 266590, ChinaFirst Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, ChinaDeep 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.https://www.mdpi.com/2072-4292/11/11/1325convolutional neural network (CNN)Fletcher–Reeves algorithm (F–R)conjugate gradientcoastal wetland classificationhyperspectral imagery |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Chen Chen Yi Ma Guangbo Ren |
spellingShingle |
Chen Chen Yi Ma Guangbo Ren A Convolutional Neural Network with Fletcher–Reeves Algorithm for Hyperspectral Image Classification Remote Sensing convolutional neural network (CNN) Fletcher–Reeves algorithm (F–R) conjugate gradient coastal wetland classification hyperspectral imagery |
author_facet |
Chen Chen Yi Ma Guangbo Ren |
author_sort |
Chen Chen |
title |
A Convolutional Neural Network with Fletcher–Reeves Algorithm for Hyperspectral Image Classification |
title_short |
A Convolutional Neural Network with Fletcher–Reeves Algorithm for Hyperspectral Image Classification |
title_full |
A Convolutional Neural Network with Fletcher–Reeves Algorithm for Hyperspectral Image Classification |
title_fullStr |
A Convolutional Neural Network with Fletcher–Reeves Algorithm for Hyperspectral Image Classification |
title_full_unstemmed |
A Convolutional Neural Network with Fletcher–Reeves Algorithm for Hyperspectral Image Classification |
title_sort |
convolutional neural network with fletcher–reeves algorithm for hyperspectral image classification |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2019-06-01 |
description |
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. |
topic |
convolutional neural network (CNN) Fletcher–Reeves algorithm (F–R) conjugate gradient coastal wetland classification hyperspectral imagery |
url |
https://www.mdpi.com/2072-4292/11/11/1325 |
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