Change Detection between Multimodal Remote Sensing Data Using Siamese CNN

2018/07/25 by Zhenchao Zhang, Zhang, Zhenchao, George Vosselman +8
Computer Science · Engineering · Environmental Science · #Automated Road and Building Extraction #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Remote Sensing and LiDAR Applications #Remote Sensing in Agriculture #Remote-Sensing Image Classification #cs.CV

paper · pdf · doi:10.48550/arxiv.1807.09562

arxiv created 2018/07/25 · openalex publication_date 2018/07/25 · arxiv updated 2018/07/26 · openalex created_date 2022/08/04 · openalex updated_date 2026/07/28

Abstract

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.

Related

• Detecting Urban Dynamics Using Deep Siamese Convolutional Neural Networks
• Urban Change Detection Using a Dual-Task Siamese Network and Semi-Supervised Learning
• Building Change Detection for Remote Sensing Images Using a Dual Task Constrained Deep Siamese Convolutional Network Model
• Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks
• Multi-stage progressive change detection on high resolution remote sensing imagery