Hyperspectral imaging is an efficient way to overcome the limitations of detecting different object with similar visible light texture. The study aims to expand the feasibility of hyperspectral imaging to classifying the stains and defects on mobile phone cover glass. Firstly, we extracted eight optimal spectral features by decision tree method, including 526 nm, 567 nm, 582 nm, 629 nm, 689 nm, 711 nm, 789 nm, and 888 nm. Our classification used the Random Forest modeling method (RF). Experimental results showed that, based on optimal spectral features, the precision of RF model outperformed for classifying stains and defects (Precision > 0.9). Overall, this study contributes a reliable and convenient tool for classification of stains and defects on mobile phone cover glass, offering scientific insights to support quality control inspection for mobile phone cover glass.
High-accuracy forest type mapping is crucial for forest resource inventory to support forest management, conservation biology, and ecological restoration. The Chinese GF-1 satellite can provide high-spatial resolution and low-cost data for forestry monitoring. However, it is difficult to obtain time-series GF-1 images with high spatial resolution and high temporal frequency because of technical constraints and cloud contamination, which affects the accuracy of forest type extraction. We proposed a method for mapping forest type using Chinese GF-1/widefield view (WFV) multi-spectral data, the fused GF-1/WFV normalized difference vegetation index (NDVI) time series, and the phenological parameters. We used the enhanced spatial and temporal adaptive reflectance fusion model algorithm to fuse Chinese GF-1/WFV and Moderate Resolution Imaging Spectroradiometer NDVI to generate high-spatiotemporal resolution NDVI time-series image data and extract phenological parameters in the northeastern part of Hubei Province, China. We then designed four different combinations of GF-1/WFV spectral features, the fused NDVI time-series features, and the phenological parameters. Finally, a random forest algorithm is used to classify forest types in the study area. The results showed that the proposed method can obtain satisfactory results. The overall accuracy based on the combination of GF-1/WFV spectral features, high-resolution time-series NDVI, and phenological features was 89.68%. Compared with the classification using only GF-1/WFV spectral data, the overall accuracy was improved by 9.60% points. These results demonstrate that the proposed method can support forest type survey and mapping at the regional or global scale.
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