Remote Sensing Image Classification for Land Cover Mapping in Developing Countries: A Novel Deep Learning Approach

被引:0
|
作者
Lynda, Nzurumike Obianuju [1 ]
Nnanna, Nwojo Agwu [1 ]
Boukar, Moussa Mahamat [1 ]
机构
[1] Nile Univ Nigeria, Dept Comp Sci, Abuja, Nigeria
来源
INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND NETWORK SECURITY | 2022年 / 22卷 / 02期
关键词
Convolutional neural network; remote sensing image classification; Land cover mapping; medium-resolution;
D O I
10.22937/IJCSNS.2022.22.2.28
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) are a category of deep learning networks that have proven very effective in computer vision tasks such as image classification. Notwithstanding, not much has been seen in its use for remote sensing image classification in developing countries. This is majorly due to the scarcity of training data. Recently, transfer learning technique has successfully been used to develop state-of-the art models for remote sensing (RS) image classification tasks using training and testing data from well-known RS data repositories. However, the ability of such model to classify RS test data from a different dataset has not been sufficiently investigated. In this paper, we propose a deep CNN model that can classify RS test data from a dataset different from the training dataset. To achieve our objective, we first, re-trained a ResNet-50 model using EuroSAT, a large-scale RS dataset to develop a base model then we integrated Augmentation and Ensemble learning to improve its generalization ability. We further experimented on the ability of this model to classify a novel dataset (Nig_Images). The final classification results shows that our model achieves a 96% and 80% accuracy on EuroSAT and Nig_Images test data respectively. Adequate knowledge and usage of this framework is expected to encourage research and the usage of deep CNNs for land cover mapping in cases of lack of training data as obtainable in developing countries.
引用
收藏
页码:214 / 222
页数:9
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