ISSN 1000-1239 CN 11-1777/TP

• 网络技术 •

### 基于多源遥感时空谱特征融合的滑坡灾害检测方法

1. 1(陆军勤务学院 重庆 401311);2(重庆大学计算机学院 重庆 400044);3(国防科技大学电子对抗学院 合肥 230037);4(78092部队 成都 610031) (chengshanjing_11@163.com)
• 出版日期: 2020-09-01
• 基金资助:
国家自然科学基金项目(61872447);重庆市自然科学基金项目(CSTC2018JCYJA1879);重庆市教委科学技术研究项目(KJQN201912905)

### Multi-Source Remote Sensing Based Accurate Landslide Detection Leveraging Spatial-Temporal-Spectral Feature Fusion

Chen Shanjing1, Xiang Chaocan2, Kang Qing1, Wu Tao3, Liu Kai2, Feng Liang2, Deng Tao4

1. 1(Army Logistics University, Chongqing 401311);2(College of Computer Science, Chongqing University, Chongqing 400044);3(College of Electronic Warfare, National University of Defense Technology, Hefei 230037);4(78092 Troop, Chengdu 610031)
• Online: 2020-09-01
• Supported by:
This work was supported by the National Natural Science Foundation of China (61872447), the Natural Science Foundation of Chongqing (CSTC2018JCYJA1879), and the Science and Technology Research Program of Chongqing Municipal Education Commission (KJQN201912905).

Abstract: Accurate landslide detection is extremely important in emergency rescue. Aiming at the problems concerning current landslide remote sensing detection that the fusion and utilization of spatial, temporal, and spectral features are poor in target detection model and the accuracy of recognition is unsatisfied, in this paper, we propose an accurate landslide detection method based on multi-source remote sensing images, leveraging the fusion of spatial, temporal, and spectral features. In specific, we construct a new multi-bands remote sensing image dataset, exploiting the registration of spectral and scale spaces based on the remote sensing image before and after landslide. Moreover, we combine the features of temporal variation, spectrum and spatial shape, which are transformed into the spectral representational model. And then, the support vector machine (SVM) algorithm is used to identify the landslide objects based on this new image dataset and representational model. Furthermore, we use the typical shape features, such as the axial aspect ratio, area and invariant moment, which are extracted for the fundamental shape models of landslides, to further classify these landslide objects. Finally, we conduct extensive experiments to evaluate the performance of the propose method by comparing with baseline methods. The experimental results show that our method outperforms the baseline algorithms, while achieving up to 95% accuracy in landslide detection.