Siamese Attention U-Net for Multi-Class Change Detection

Sol Cummings, Lukas Kondmann, Xiao Xiang Zhu

Abstract: Recent developments in deep learning have pushed the capabilities of pixel-wise change detection. This work introduces the winning solution of the DynamicEarthNet Weakly-Supervised Multi-Class Change Detection Challenge held at the EARTHVISION Workshop in CVPR 2021. The proposed approach is a pixel-wise change detection network coined Siamese Attention U-Net that incorporates attention mechanisms in the Siamese U-Net architecture. Moreover, this work finds the location of the attention mechanism within the network is crucial in achieving higher performance. Positioning the attention blocks in the up-sample path of the decoder filters noisy lower resolution features and allows for more fine-grained outputs. The impact of architectural changes, alongside training strategies such as semi-supervised learning are also evaluated on the DynamicEarthNet Challenge dataset. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Code is available at: https://github.com/solcummings/earthvision2021-weakly-supervised.