Automatic Case-Based Reasoning Approach for Landslide Detection: Integration of Object-Oriented Image Analysis and a Genetic Algorithm

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 OPEN ACCESS Remote Sens. 2015, 7 4319 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.

Monitoring Forest Dynamics in the Andean Amazon: The Applicability of Breakpoint Detection Methods Using Landsat Time-Series and Genetic Algorithms

“…For this reason, our research also constitutes a novel approach for solving the calibration of complex models in remote sensing by reducing uncertainties through parametrization. This method is different from other optimization approaches in remote sensing, where the principal application is classification and pattern recognition [8][9][10]; however, it is closer to the approach of Reference [15] for calibrating a model of cellular automata.…”

“…Nonetheless, optimization has been shown to be successful in remote sensing. Examples vary according to the field and the search algorithm applied [8][9][10]. Genetic algorithms (GAs) have a long history of refinement since it became popular though the work of Holland [11]; extensive research has reported it as a robust and efficient optimization algorithm with a wide range of application in areas such as engineering, numerical optimization, robotics, classification, pattern recognition, and product design, among others [12,13].…”

Using an Unmanned Aerial Vehicle-Based Digital Imaging System to Derive a 3D Point Cloud for Landslide Scarp Recognition

“…The abovementioned methods are less effective than Light Detection and Ranging (LiDAR) at detecting large landslides that have experienced significant historical activity [33]. The use of LiDAR technology for landslide identification has become increasingly popular in digital terrain modelling [12,[34][35][36][37][38][39][40]. LiDAR is a valuable tool in geology [41], geomorphology [12], and hazard reduction efforts.…”

A Novel Hybrid Approach Based on Instance Based Learning Classifier and Rotation Forest Ensemble for Spatial Prediction of Rainfall-Induced Shallow Landslides using GIS

“…The most common data mining methods used in landslide modeling are artificial neural networks [11,15,16], support vector machines [17][18][19][20][21], decision trees [10,20,22], and neuro-fuzzy [23,24]. Literature review shows that new data mining algorithms are suitable for landslide modeling for large and complex areas with good results [3,[25][26][27][28][29][30], and, in general, data mining models outperform conventional methods [10,[31][32][33]. However, recent studies on landslide modeling show that the overall performance of prediction models could be enhanced with the use of ensemble frameworks [31,34,35].…”