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Fully Convolutional Siamese Networks for Change Detection

2018-10-19Code Available0· sign in to hype

Rodrigo Caye Daudt, Bertrand Le Saux, Alexandre Boulch

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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.

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Benchmark Results

DatasetModelMetricClaimedVerifiedStatus
CLCDFC-Siam-diffF154.1Unverified
EGY-BCDFC-Siam-diffF142.3Unverified
GVLMFC-Siam-diffF174.3Unverified
OSCD - 13chFC-EFF156.91Unverified
OSCD - 13chFC-Siam-DiffF157.92Unverified
OSCD - 3chFC-EFF148.89Unverified
OSCD - 3chFC-Siam-diffPrecision49.81Unverified

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