Change Detection Using Multi-Source Remotely Sensed Imagery

Dear Colleagues,

The earth’s environments are experiencing unprecedented changes due to intensive urban development, farming, habitat loss, pollution, and climate change. Therefore, a better identification of such changes is imperative. Due to the repeat-pass nature of the sensor platforms, remote sensing imagery seems to be an ideal data source for change detection. With the development of remote sensing imaging techniques, multi-source data, such as optical, SAR, LiDAR, video, which are installed on satellites, aircraft, UAV, ground platforms, are increasingly available. Additionally, owing to large coverage, representation of local knowledge, and open data policies, volunteered and crowdsourcing geographic information data, such as those provided by Open Street Map, Wikimapia, and Google Map Maker, and social network data (provided by Facebook, Twitter, Weibo, etc.), are growing in volume and availability. These freely available geographic datasets can supply ancillary data to assist remote sensing image change detection.

In this context, ever-expanding choices of multi-source data can be considered in the change detection task. Since multi-source data are able to bring complementary information of the same scene, change detection, based on multi-source data, can achieve increased robustness and accuracy compared with those techniques based on a single source. By integrating temporal, spatial, spectral, and semantic information, change detection using multi-source data becomes a promising research subject. However, change detection using multi-source data, especially multi-modal data, remains challenging because of temporal inconsistency, spectral and spatial variations, differences in imaging mechanisms, and difficulty in co-registration. Therefore, the inclusion of a Special Issue in the journal Remote Sensing is timely to promote innovation and improvement of change detection using multi-source data. In this Special Issue, we aim to cover the latest advances and trends in the field of change detection using multi-source remotely sensed imagery.

Prof. Xin Huang
Dr. Jiayi Li
Dr. Francesca Bovolo
Dr. Qi Wang
Guest Editors

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