## 1 Introduction  
Landslides are dangerous geological disasters with catastrophic effects on human lives and properties. Landslides occur with high frequency in mountainous and hilly areas, such as the Cameron Highlands in Malaysia. Landslide incidence is related to a cluster of triggering factors, such as intense rainfall, volcanic eruptions, rapid snowmelt, elevated water levels, and earthquakes. Landslide inventory maps are crucial for measuring the magnitude and analyzing the susceptibility, hazard, and risk of earthquakes [1; 2], as well as for examining distribution patterns and predicting the landscapes affected by landslide [3]. Mapping a landslide inventory in tropical areas is challenging because the dense vegetation cover in these regions obscures underlying landforms [4]. Moreover, the majority of available conventional landslide detection techniques are not rapid and accurate enough for inventory mapping given the rapid vegetation growth in tropical regions. Therefore, inventory mapping requires the use of more rapid and accurate techniques, such as light detection and ranging (LiDAR) [5], which uses active laser transmitters and receivers to acquire elevation data. In addition, LiDAR has the unique capability to penetrate densely vegetated areas [5] and provide detailed information on terrains with high point density. Moreover, it depicts ground surface features and provides useful information on topographical features in areas where landslide locations are obscured by vegetation cover [6; 7]. Numerous studies have applied a multiresolution segmentation algorithm for the remote sensing of land features [8]. This algorithm requires the identification of three parameters (i.e., scale, shape, and compactness); the values of these parameters can be determined using the traditional trial-and-error method, which is very time consuming and laborious [5]. Moreover, using the algorithm to delineate the boundary of an object at different scales remains challenging [9]. Thus, optimal parameters for segmentation should be identified via semiautomatic and automatic approaches [10; 11; 12]. The automatic selection of segmentation parameters requires the use of the advanced supervised approach presented in [13]. Processing a large number of irrelevant features causes overfitting [14]. By contrast, the best classification results are obtained by selecting the most relevant feature [15]. Landslide identification in a particular area can be improved by selecting the most significant feature [15; 16]. As shown in [2], selecting the most significant feature facilitates the differentiation of landslides from non-landslides. Accuracy can be improved by decreasing the number of features, as recommended in [17]. The efficiency of feature selection techniques for landslide detection has been proven in [18; 19; 20]. The neural network (NN) is effective in remote sensing applications [21], particularly in solving different image classification problems [22] specified by nonlinear mathematical fitting for function approximation. NN architectures are classified into the recurrent neural network (RNN), back-propagation neural network, probability neural network, and multilayer perceptron neural network (MLP-NN). NN-based classifiers can adapt to different types of data and inputs, and can overcome the issue of mixed pixels by providing fuzzy output and fit with multiple images [23; 24]. These classifiers include parallel computation, which is superior to statistical classification approaches because it is non-parametric and does not require the prior knowledge of a distribution model for input data [25]. Moreover, NN-based classifiers can evaluate non-linear relationships between the input data and desired outputs and are distinguished by their fast generalization capability [26]. NN-based classifiers have been successfully in function approximation, prediction, pattern recognition, landslide detection, image classification, automatic control, and landslide susceptibility [27; 28; 29; 30; 31; 32].