Abstract
Detecting topographic changes in an urban environment and keeping city-level point clouds up-to-date are important tasks for urban planning and monitoring. In practice, remote sensing data are often available only in different modalities for two epochs. Change detection between airborne laser scanning data and photogrammetric data is challenging due to the multi-modality of the input data and dense matching errors. This paper proposes a method to detect building changes between multimodal acquisitions. The multimodal inputs are converted and fed into a light-weighted pseudo-Siamese convolutional neural network (PSI-CNN) for change detection. Different network configurations and fusion strategies are compared. Our experiments on a large urban data set demonstrate the effectiveness of the proposed method. Our change map achieves a recall rate of 86.17%, a precision rate of 68.16%, and an F1-score of 76.13%. The comparison between Siamese architecture and feed-forward architecture brings many interesting findings and suggestions to the design of networks for multimodal data processing.
Highlights
Detecting topographic changes and keeping topographic databases up-to-date in large-scale urban scenes are fundamental tasks in urban planning and environmental monitoring [1,2]
This is applicable to the situation of several mapping agencies, where laser scanning data are already available as archive data, while aerial images are routinely acquired every one or two years for updates
Towards the end of training, the model with the highest F1 -score was selected as the final trained model
Summary
Detecting topographic changes and keeping topographic databases up-to-date in large-scale urban scenes are fundamental tasks in urban planning and environmental monitoring [1,2]. The remote sensing data available at different epochs over a same region are often acquired with different modalities (i.e., with different platforms and sensor characteristics). Such heterogeneity makes the detection of changes between such multimodal remote sensing data very challenging. This paper aims to detect building changes between ALS data and airborne photogrammetric data. This is applicable to the situation of several mapping agencies, where laser scanning data are already available as archive data, while aerial images are routinely acquired every one or two years for updates. Since the ALS data are generally more accurate and contain less noise compared to dense image
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