Abstract
This paper proposes an automatic method for detecting landslides by using an integrated approach comprising object-oriented image analysis (OOIA), a genetic algorithm (GA), and a case-based reasoning (CBR) technique. It consists of three main phases: (1) image processing and multi-image segmentation; (2) feature optimization; and (3) detecting landslides. The proposed approach was employed in a fast-growing urban region, the Pearl River Delta in South China. The results of detection were validated with the help of field surveys. The experimental results indicated that the proposed OOIA-GA-CBR (0.87) demonstrates higher classification performance than the stand-alone OOIA (0.75) method for detecting landslides. The area under curve (AUC) value was also higher than that of the simple OOIA, indicating the high efficiency of the proposed landslide detection approach. The case library created using the integrated model can be reused for time-independent analysis, thus rendering our approach superior in comparison to other traditional methods, such as the maximum likelihood classifier. The results of this study thus facilitate fast generation of accurate landslide inventory maps, which will eventually extend our understanding of the evolution of landscapes shaped by landslide processes.
Highlights
Landslides are one of the most destructive natural hazards, and they often cause substantial damage to societies worldwide every year [1,2]
Preparing landslide inventory maps is necessary to archive the extent of landslide phenomena in specific areas; to examine their spatial distribution and types, risk, vulnerability, recurrence, and statistical slope instability; and to investigate the evolution of landscapes controlled by landslide processes [4,7,8]
The chosen scales were tested by running Estimation of Scale Parameter (ESP) tools, and the results indicated that the images were segmented appropriately by using the bottom-up region-merging strategy
Summary
Landslides are one of the most destructive natural hazards, and they often cause substantial damage to societies worldwide every year [1,2]. The intensity of landslides results in more substantial injuries and loss of life than any other type of natural disaster, including earthquakes, hurricanes, tsunamis, and floods [3]. This trend is likely to worsen in the future with the process of urbanization and economic development, deforestation, and the increased regional rainfall in landslide-prone areas caused by climate change [1]. Landslide inventory maps have largely been generated through visual interpretation of aerial photos or satellite images combined with extensive field surveys Such methods are labor-intensive and expensive and, inefficient for generating maps of large areas. The advantage of EKSs is in task specific knowledge; a limitation of implementing EKS methods is that identifying and defining rules for each separate problem is tedious and time consuming [14,15]
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