Land-Use Classification Using Convolutional Neural Networks

被引：3

作者：

Stepchenko, A. M. ^{[1
]}

机构：

[1] Ventspils Univ Appl Sci, Engn Res Inst, Dept Remote Sensing, Ventspils Int Radioastron Ctr, Ventspils, Latvia

来源：

AUTOMATIC CONTROL AND COMPUTER SCIENCES | 2021年 / 55卷 / 04期

关键词：

convolutional neural networks (CNN); feature extraction; land-use classification;

D O I：

10.3103/S0146411621040088

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Convolutional neural networks (CNNs) have been used in several classification tasks. This study aims to evaluate the performance of CNN methods for land-use classification. CNN-based model was evaluated on aerial orthophoto data for land-use scene classification. Ground-truth data set containing 25 253 records with known land-use category were used to train the CNN model to solve a practical issue. The overall accuracy of the best model on the test data set was 94.00%. The obtained results indicated that CNN mode showed high accuracy and is suitable for land-use classification tasks.

引用

页码：358 / 367

页数：10

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