Abstract
Abstract. Change detection is a very important problem for the remote sensing community. Among the several approaches proposed during recent years, deep learning provides methods and tools that achieve state of the art performances. In this paper, we tackle the problem of urban change detection by constructing a fully convolutional multi-task deep architecture. We present a framework based on the UNet model, with fully convolutional LSTM blocks integrated on top of every encoding level capturing in this way the temporal dynamics of spatial feature representations at different resolution levels. The proposed network is modular due to shared weights which allow the exploitation of multiple (more than two) dates simultaneously. Moreover, our framework provides building segmentation maps by employing a multi-task scheme which extracts additional feature attributes that can reduce the number of false positive pixels. We performed extensive experiments comparing our method with other state of the art approaches using very high resolution images of urban areas. Quantitative and qualitative results reveal the great potential of the proposed scheme, with F1 score outperforming the other compared methods by almost 2.2%.
Highlights
Urban change detection is one of the most studied topics in remote sensing since it provides useful insights concerning the cities’ growing patterns and future tendencies
The high availability of earth observation data has enabled the remote sensing community to collect multimodal, multitemporal satellite images laying in this way the foundation for constructive research studies
Every encoding level Ei with i ∈ {1, 2, .., n} produces spatial feature vectors Xit for t ∈ {1, 2, .., D}. These feature vectors are fed to a Long Short Term Memory (LSTM) block which is added as a skip connection on top of every encoding level, determining the temporal attributes using a gating mechanism (Hochreiter, Schmidhuber, 1997)
Summary
Urban change detection is one of the most studied topics in remote sensing since it provides useful insights concerning the cities’ growing patterns and future tendencies. The high availability of earth observation data has enabled the remote sensing community to collect multimodal, multitemporal satellite images laying in this way the foundation for constructive research studies To this day, manual change detection approaches have been replaced with automatic supervised and unsupervised algorithms such as graphical models and Markov Random Fields (Singh et al, 2014, Benedek et al, 2015, Vakalopoulou et al, 2016, Vakalopoulou et al, 2015, Karantzalos, 2015), kernels (Volpi et al, 2012), as well as Principal Component Analysis (Li, Yeh, 1998, Deng et al, 2008). In (Caye Daudt et al, 2018b), a patch-based framework is suggested where two different architectures (Siamese and Early Fusion) are examined using the Onera Satellite Change Detection bi-temporal
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