Classification of Very High Resolution Aerial Photos Using Spectral-Spatial Convolutional Neural Networks
Joint Authors
Pradhan, Biswajeet
Sameen, Maher Ibrahim
Aziz, Omar Saud
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-06-26
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Classification of aerial photographs relying purely on spectral content is a challenging topic in remote sensing.
A convolutional neural network (CNN) was developed to classify aerial photographs into seven land cover classes such as building, grassland, dense vegetation, waterbody, barren land, road, and shadow.
The classifier utilized spectral and spatial contents of the data to maximize the accuracy of the classification process.
CNN was trained from scratch with manually created ground truth samples.
The architecture of the network comprised of a single convolution layer of 32 filters and a kernel size of 3 × 3, pooling size of 2 × 2, batch normalization, dropout, and a dense layer with Softmax activation.
The design of the architecture and its hyperparameters were selected via sensitivity analysis and validation accuracy.
The results showed that the proposed model could be effective for classifying the aerial photographs.
The overall accuracy and Kappa coefficient of the best model were 0.973 and 0.967, respectively.
In addition, the sensitivity analysis suggested that the use of dropout and batch normalization technique in CNN is essential to improve the generalization performance of the model.
The CNN model without the techniques above achieved the worse performance, with an overall accuracy and Kappa of 0.932 and 0.922, respectively.
This research shows that CNN-based models are robust for land cover classification using aerial photographs.
However, the architecture and hyperparameters of these models should be carefully selected and optimized.
American Psychological Association (APA)
Sameen, Maher Ibrahim& Pradhan, Biswajeet& Aziz, Omar Saud. 2018. Classification of Very High Resolution Aerial Photos Using Spectral-Spatial Convolutional Neural Networks. Journal of Sensors،Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1201941
Modern Language Association (MLA)
Sameen, Maher Ibrahim…[et al.]. Classification of Very High Resolution Aerial Photos Using Spectral-Spatial Convolutional Neural Networks. Journal of Sensors No. 2018 (2018), pp.1-12.
https://search.emarefa.net/detail/BIM-1201941
American Medical Association (AMA)
Sameen, Maher Ibrahim& Pradhan, Biswajeet& Aziz, Omar Saud. Classification of Very High Resolution Aerial Photos Using Spectral-Spatial Convolutional Neural Networks. Journal of Sensors. 2018. Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1201941
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1201941