Landslide Detection Using a Saliency Feature Enhancement Technique From LiDAR-Derived DEM and Orthophotos
| Autor: | Mustafa Ridha Mezaal, Abdullah Al-Amri, Husam Abdulrasool H. Al-Najjar, Biswajeet Pradhan, Maher Ibrahim Sameen |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
LiDAR
010504 meteorology & atmospheric sciences General Computer Science 0211 other engineering and technologies 02 engineering and technology Landslide detection 01 natural sciences remote sensing General Materials Science Digital elevation model Cluster analysis 021101 geological & geomatics engineering 0105 earth and related environmental sciences Remote sensing Pixel saliency feature enhancement General Engineering Orthophoto Landslide Ranging GIS Lidar Feature (computer vision) lcsh:Electrical engineering. Electronics. Nuclear engineering lcsh:TK1-9971 Geology |
| Zdroj: | IEEE Access, Vol 8, Pp 121942-121954 (2020) |
| ISSN: | 2169-3536 |
| Popis: | This study proposes a new landslide detection technique that is semi-automated and based on a saliency enhancement approach. Unlike most of the landslide detection techniques, the approach presented in this paper is simple yet effective and does not require landslide inventory data for training purposes. It comprises several steps. First, it enhances potential landslide pixels. Then, it removes the image background using slope information derived from a very high-resolution LiDAR-based (light detection and ranging) digital elevation model (DEM). After that, morphological analysis was applied to remove small objects, separate landslide objects from each other, and fill the gaps between large bare soil objects and urban objects. Finally, landslide scars were detected using the Fuzzy C-means (FCM) clustering algorithm. The proposed method was developed based on datasets acquired over the Kinta Valley area in Malaysia and tested on another area with a different environment and topography (i.e., Cameron Highlands). The results showed that the proposed landslide detection technique could detect landslides in the training area with a Prediction Accuracy, Kappa index, and Mean Intersection-Over-Union (mIOU) of 71.12%, 0.81, and 68.52%, respectively. The Prediction Accuracy, Kappa index, and mIOU of the method based on the test dataset were 65.78%, 0.68, and 56.14%, respectively. These results show that the proposed method can be used for landslide inventory mapping and risk assessments. |
| Databáze: | OpenAIRE |
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