Change Detection between Multimodal Remote Sensing Data Using Siamese CNN

TL;DR

This paper introduces a framework using Siamese CNNs to detect changes in buildings and trees from multimodal remote sensing data, effectively handling different data characteristics across epochs.

Contribution

It presents a novel approach combining 2D and 3D data transformation with Siamese CNNs for accurate change detection in multimodal remote sensing data.

Findings

86.4% of patch pairs correctly classified

Effective detection of building and tree changes

Handles multimodal data with different characteristics

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.