· 2018
Fully Convolutional Siamese Networks for Change Detection
- 1,585citations
- 2018year
Short summary
Two novel Siamese fully convolutional networks achieve state-of-the-art change detection performance on RGB and multispectral images, outperforming prior methods by at least 500x in speed.
AI-generated from the title and abstract; the full text is not read.
Key points
- Introduced three fully convolutional neural network architectures for image change detection.
- Developed two Siamese fully convolutional network extensions tailored for change detection.
- Achieved state-of-the-art results on two open change detection datasets using RGB and multispectral images.
- System learns from scratch using annotated change detection images.
- Proposed architectures are at least 500 times faster than related systems.
AI-generated from the title and abstract; the full text is not read.
Abstract
This paper presents three fully convolutional neural network architectures which perform change detection using a pair of coregistered images. Most notably, we propose two Siamese extensions of fully convolutional networks which use heuristics about the current problem to achieve the best results in our tests on two open change detection datasets, using both RGB and multispectral images. We show that our system is able to learn from scratch using annotated change detection images. Our architectures achieve better performance than previously proposed methods, while being at least 500 times faster than related systems. This work is a step towards efficient processing of data from large scale Earth observation systems such as Copernicus or Landsat.
The authors' abstract, as published at the source., 2018 · DOI ↗
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Field: Media Technology
Media TechnologyEngineering