138,565 research outputs found
Weakly Supervised Silhouette-based Semantic Scene Change Detection
This paper presents a novel semantic scene change detection scheme with only
weak supervision. A straightforward approach for this task is to train a
semantic change detection network directly from a large-scale dataset in an
end-to-end manner. However, a specific dataset for this task, which is usually
labor-intensive and time-consuming, becomes indispensable. To avoid this
problem, we propose to train this kind of network from existing datasets by
dividing this task into change detection and semantic extraction. On the other
hand, the difference in camera viewpoints, for example, images of the same
scene captured from a vehicle-mounted camera at different time points, usually
brings a challenge to the change detection task. To address this challenge, we
propose a new siamese network structure with the introduction of correlation
layer. In addition, we create a publicly available dataset for semantic change
detection to evaluate the proposed method. The experimental results verified
both the robustness to viewpoint difference in change detection task and the
effectiveness for semantic change detection of the proposed networks. Our code
and dataset are available at https://github.com/xdspacelab/sscdnet.Comment: Accepted at the 2020 IEEE International Conference on Robotics and
Automation (ICRA). Code and dataset are available at
https://github.com/xdspacelab/sscdne
Joint Spatio-Temporal Modeling for the Semantic Change Detection in Remote Sensing Images
Semantic Change Detection (SCD) refers to the task of simultaneously
extracting the changed areas and the semantic categories (before and after the
changes) in Remote Sensing Images (RSIs). This is more meaningful than Binary
Change Detection (BCD) since it enables detailed change analysis in the
observed areas. Previous works established triple-branch Convolutional Neural
Network (CNN) architectures as the paradigm for SCD. However, it remains
challenging to exploit semantic information with a limited amount of change
samples. In this work, we investigate to jointly consider the spatio-temporal
dependencies to improve the accuracy of SCD. First, we propose a Semantic
Change Transformer (SCanFormer) to explicitly model the 'from-to' semantic
transitions between the bi-temporal RSIs. Then, we introduce a semantic
learning scheme to leverage the spatio-temporal constraints, which are coherent
to the SCD task, to guide the learning of semantic changes. The resulting
network (SCanNet) significantly outperforms the baseline method in terms of
both detection of critical semantic changes and semantic consistency in the
obtained bi-temporal results. It achieves the SOTA accuracy on two benchmark
datasets for the SCD
Semantic Approach in Image Change Detection
International audienceChange detection is a main issue in various domains, and especially for remote sensing purposes. Indeed, plethora of geospatial images are available and can be used to update geographical databases. In this paper, we propose a classification-based method to detect changes between a database and a more recent image. It is based both on an efficient training point selection and a hierarchical decision process. This allows to take into account the intrinsic heterogeneity of the objects and themes composing a database while limiting false detection rates. The reliability of the designed framework method is first assessed on simulated data, and then successfully applied on very high resolution satellite images and two land-cover databases
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