Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 17, Issue 8, Pages 1455-1459Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2019.2945906
Keywords
Unmanned aerial vehicles; Feature extraction; Remote sensing; Satellites; Task analysis; Streaming media; Convolution; Aerial images; change detection; fully convolutional neural networks (FCNNs); fully convolutional Siamese networks; unmanned aerial vehicles (UAVs)
Categories
Funding
- CAPES
- FAPEMIG
- CNPq
- Petrobras
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Different applications in remote sensing, such as crop monitoring and visual surveillance, demand the automatic detection of changes from sets of images acquired over time. Most traditional approaches use satellite imagery, which, besides the known issues such as cloud cover and image acquisition frequency for nongeostationary satellites, are very costly. In this context, with the recent technological advances, unmanned aerial vehicles (UAVs) have become ubiquitous in numerous applications. In this letter, we present a fully convolutional Siamese autoencoder method for change detection in aerial images, in particular for those obtained with UAVs. We show that, by using an autoencoder, we can further reduce the number of labeled samples required to achieve competitive results. We evaluated the performance of our approach on two different data sets, and the results showed that our methodology outperforms the state of the art, while demanding less training data.
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