Change Detection between Multimodal Remote Sensing Data Using Siamese CNN
This addresses urban planning and monitoring challenges by enabling change detection between 3D laser scanning and 2D imagery, though it is incremental as it adapts existing methods to multimodal data.
The paper tackles the problem of detecting topographic changes in urban environments using multimodal remote sensing data from different epochs, achieving 86.4% correct classification of patch pairs with a Siamese CNN-based framework.
Detecting topographic changes in the urban environment has always been an important task for urban planning and monitoring. In practice, remote sensing data are often available in different modalities and at different time epochs. Change detection between multimodal data can be very challenging since the data show different characteristics. Given 3D laser scanning point clouds and 2D imagery from different epochs, this paper presents a framework to detect building and tree changes. First, the 2D and 3D data are transformed to image patches, respectively. A Siamese CNN is then employed to detect candidate changes between the two epochs. Finally, the candidate patch-based changes are grouped and verified as individual object changes. Experiments on the urban data show that 86.4\% of patch pairs can be correctly classified by the model.