ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-2-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 541–547, 2020
https://doi.org/10.5194/isprs-annals-V-2-2020-541-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 541–547, 2020
https://doi.org/10.5194/isprs-annals-V-2-2020-541-2020

  03 Aug 2020

03 Aug 2020

URBAN CHANGE DETECTION BASED ON SEMANTIC SEGMENTATION AND FULLY CONVOLUTIONAL LSTM NETWORKS

M. Papadomanolaki1,2, M. Vakalopoulou2,3, and K. Karantzalos1 M. Papadomanolaki et al.
  • 1Remote Sensing Laboratory, National Technical University of Athens, Zographos, Greece
  • 2Université Paris-Saclay, CentraleSupélec, MICS Laboratory, Gif-sur-Yvette, France
  • 3Université Paris-Saclay, CentraleSupélec, Inria, Gif-sur-Yvette, France

Keywords: change detection, buildings segmentation, multi-task learning, deep learning, fully convolutional LSTMs, very high resolution imagery

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%.