Abstract
Using remote sensing techniques to monitor landslides and their resultant land cover changes is fundamentally important for risk assessment and hazard prevention. Despite enormous efforts in developing intelligent landslide mapping (LM) approaches, LM remains challenging owing to high spectral heterogeneity of very-high-resolution (VHR) images and the daunting labeling efforts. To this end, a deep learning model based on semi-supervised multi-temporal deep representation fusion network, namely SMDRF-Net, is proposed for reliable and efficient LM. In comparison with previous methods, the SMDRF-Net possesses three distinct properties. (1) Unsupervised deep representation learning at the pixel- and object-level is performed by transfer learning using the Wasserstein generative adversarial network with gradient penalty to learn discriminative deep features and retain precise outlines of landslide objects in the high-level feature space. (2) Attention-based adaptive fusion of multi-temporal and multi-level deep representations is developed to exploit the spatio-temporal dependencies of deep representations and enhance the feature representation capability of the network. (3) The network is optimized using limited samples with pseudo-labels that are automatically generated based on a comprehensive uncertainty index. Experimental results from the analysis of VHR aerial orthophotos demonstrate the reliability and robustness of the proposed approach for LM in comparison with state-of-the-art methods.
Highlights
IntroductionLandslide mapping (LM) can be regarded as the quantification of land cover changes that are automatically derived from pre- and post-event
With respect to supervised GANbased (SGAN), false alarms can be reduced to some extent, as illustrated in Figure 7d,j, its use still results in the misdetection of some landslide regions because discriminators obtained through adversarial training are used to extract deep features
Compared to change-detection-based MRF (CDMRF), object-based majority voting (OMV), and SGAN, the superpixel-based difference representation learning (SDRL) which uses local and high-level representations from deep neural networks offers effective detection of homogenous landslide regions, but it still suffers from losses of detailed information in the boundaries, as shown in Figures 6c and 9c
Summary
Landslide mapping (LM) can be regarded as the quantification of land cover changes that are automatically derived from pre- and post-event. LM records the attribute information of landslides, including the location, spatial extent, size, type, and date of occurrence [8,9]. This information is essential for quantitative hazard and risk assessment. Land cover change detection (CD) techniques based on multi-temporal RS datasets are usually selected to identify the differences between the pre- and post-event RS imagery and these changes are attributed to the landslide occurrence.
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