Abstract
This paper puts forward a new method of landscape recognition and evaluation by using aerial video and EEG technology. In this study, seven typical landscape types (forest, wetland, grassland, desert, water, farmland, and city) were selected. Different electroencephalogram (EEG) signals were generated through different inner experiences and feelings felt by people watching video stimuli of the different landscape types. The electroencephalogram (EEG) features were extracted to obtain the mean amplitude spectrum (MAS), power spectrum density (PSD), differential entropy (DE), differential asymmetry (DASM), rational asymmetry (RASM), and differential caudality (DCAU) in the five frequency bands of delta, theta, alpha, beta, and gamma. According to electroencephalogram (EEG) features, four classifiers including the back propagation (BP) neural network, k-nearest neighbor classification (KNN), random forest (RF), and support vector machine (SVM) were used to classify the landscape types. The results showed that the support vector machine (SVM) classifier and the random forest (RF) classifier had the highest accuracy of landscape recognition, which reached 98.24% and 96.72%, respectively. Among the six classification features selected, the classification accuracy of MAS, PSD, and DE with frequency domain features were higher than those of the spatial domain features of DASM, RASM and DCAU. In different wave bands, the average classification accuracy of all subjects was 98.24% in the gamma band, 94.62% in the beta band, and 97.29% in the total band. This study identifies and classifies landscape perception based on multi-channel EEG signals, which provides a new idea and method for the quantification of human perception.
Highlights
IntroductionLandscape classification has always been concerned with descriptive analysis involving the physical characteristics of a landscape based on basic surveys and specifications
The classification accuracy of the four classifiers for different landscape types are disdisplayed in Table 2 and Figure 4
For all EEG features, the support vector machine (SVM) classifier and random forest (RF) classifier played in Table 2 and Figure 4
Summary
Landscape classification has always been concerned with descriptive analysis involving the physical characteristics of a landscape based on basic surveys and specifications. The combination of the subjective, visual appreciation of scenery and the more objectively describable physical elements seems to have been strong resistance to the idea of landscape classification [1,2]. There seems to be no classification problems if we restrict ourselves to the more limited physical (namely landform and land-use) concept of landscape. Landscape classification of Central Europe was based on the cluster analysis of principal components, which would be used for further assessment of ecosystem services within the focus region [4]. The idea of using GEOBIA and a supervised classifier to
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