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Conference proceeding
An empirical study on Fully Convolutional Network and hypercolumn methods for UAV remote sensing imagery classification
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, pp.2567-2570
09/26/2020
Web of Science ID: WOS:000664335302148
Abstract
Fully Convolutional Network (FCN), which can adopt various Convolutional Neural Networks (CNN), are now increasingly being used in remote sensing communities. CNN are improved constantly either in accuracy or by reducing parameters for a given equivalent accuracy. This paper investigates five widely used CNNs (AlexNet, VGG16, ResNet, SqueezeNet, and a pruned VGG16) in the context of FCN for coastal beach classification of imagery acquired by Unmanned Aerial Vehicles (UAV). Our experiments show that (1) not every CNN is suitable to FCN for semantic segmentations of images though each CNN approximately achieved an equivalent accuracy for image labeling; (2) band reduced pruning of existing CNN has the least impact on implementation and accuracy. To examine the capability of convolutional layers capturing semantic features, this paper also carries out beach classification experiments using hypercolumn methods with VGG16.
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Details
- Title
- An empirical study on Fully Convolutional Network and hypercolumn methods for UAV remote sensing imagery classification
- Publication Details
- IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, pp.2567-2570
- Resource Type
- Conference proceeding
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE); United states
- Copyright
- © 2020, IEEE
- Identifiers
- WOS:000664335302148; 99380491191606600
- Academic Unit
- Hal Marcus College of Science and Engineering ; Earth and Environmental Sciences
- Language
- English