Construction waste classification method based on multiple feature combination
The rapid growth of urban areas has led to a significant rise in construction waste, causing urban pollution and waste accumulation issues. This study aims to investigate the impact of multi-feature combination methods on the accuracy of construction waste classification in hyperspectral images, focusing on a specific region in Beijing. Construction waste classification experiments are conducted using on-site spectral data collected with the ASD QualitySpec Trek handheld spectrometer and Zhuhai-1 hyperspectral images. A total of 90 feature variables, including spectral features, vegetation index, water index, and texture features, are extracted and ranked based on their importance using the mean decrease impurity. Feature variables with relatively high importance scores are selected to create a multi-feature combination vector for classification. The random forest algorithm is then employed to perform construction waste classification experiments on this vector. The experimental results reveal that the random forest classification method using multi-feature combination outperforms the traditional random forest method, achieving an overall classification accuracy of 85.86％ and a Kappa coefficient of 0.80.In comparison, the original random forest method achieves an overall classification accuracy of 83.48％ and a Kappa coefficient of 0.75, indicating the effectiveness of the random forest algorithm using multiple feature combinations.
construction wastemultiple feature combinationrandom foresthyperspectral images
万方数据
维普
