Typical Ground Object Recognition Based on Principle Component Analysis and Fuzzy Clustering with Near-Infrared Diffuse Reflectance Spectroscopy

Near infrared spectroscopy is widely applied in remote sensing and recognition. In this manuscript, the classification method which is based on principal component analysis (PCA) and fuzzy clustering is applied to recognize typical objects with near-infrared diffuse reflectance spectra. The diffuse reflectance spectra of four types of ground targets are measured in the range of 1 100~2 500 nm. Firstly, the spectral features are extractedwith PCA analysis. Secondly, the principal components is set to be the input to the fuzzy clustering model to calculate the closeness degree between different samples. Finally, the typical objects were classified based on principle of fuzzy closeness optimization. The results indicated that the proposed method is beneficial for automatic recognition and classificationof typical ground. Thenear-infrared diffuse reflectance spectroscopy reflects the feature of typical ground object. Taking the advantage of spectral features extracting and data dimensions reduction, the PCA algorithm can effectively improve the recognition efficiency. The fuzzy classification method also improve the robustness of the model. This method provides a new concept for the analysis and processing of remote sensing spectroscopy.