Name
Title | ||
|---|---|---|
Automated Landslides Detection for Mountain Cities Using Multi-Temporal Remote Sensing Imagery. |
Abstract | ||
|---|---|---|
Landslides that take place in mountain cities tend to cause huge casualties and economic losses, and a precise survey of landslide areas is a critical task for disaster emergency. However, because of the complicated appearance of the nature, it is difficult to find a spatial regularity that only relates to landslides, thus landslides detection based on only spatial information or artificial features usually performs poorly. In this paper, an automated landslides detection approach that is aiming at mountain cities has been proposed based on pre-and post-event remote sensing images, it mainly utilizes the knowledge of landslide-related surface covering changes, and makes full use of the temporal and spatial information. A change detection method using Deep Convolution Neural Network (DCNN) was introduced to extract the areas where drastic alterations have taken place; then, focusing on the changed areas, the Spatial Temporal Context Learning (STCL) was conducted to identify the landslides areas; finally, we use slope degree which is derived from digital elevation model (DEM) to make the result more reliable, and the change of DEMis used for making the detected areas more complete. The approach was applied to detecting the landslides in Shenzhen, Zhouqu County and Beichuan County in China, and a quantitative accuracy assessment has been taken. The assessment indicates that this approach can guarantee less commission error of landslide areal extentwhich is below 17.6% and achieves a quality percentage above 61.1%, and for landslide areas, the detection percentage is also competitive, the experimental results proves the feasibility and accuracy of the proposed approach for the detection landslides in mountain cities. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3390/s18030821 | SENSORS |
Keywords | Field | DocType |
landslides detection,remote sensing images,change detection,Deep Convolution Neural Network,Spatial Temporal Context Learning | Spatial analysis,Change detection,Convolutional neural network,Remote sensing,Digital elevation model,Landslide,Temporal context,Engineering | Journal |
Volume | Issue | Citations |
18 | 3.0 | 3 |
PageRank | References | Authors |
0.41 | 11 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhong Chen | 1 | 31 | 7.12 |
| Yifei Zhang | 2 | 3 | 0.41 |
| Chao Ouyang | 3 | 11 | 0.87 |
| Feng Zhang | 4 | 32 | 11.36 |
| Jie Ma | 5 | 50 | 5.90 |