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Digital cameras have a wide range of applications besides taking pictures and many scholars have focused on how to use a digital camera to recover spectral reflectance of the objects in a scene. However, it is difficult to accurately reconstruct the reflectance from the RGB values with only three dimensions. Several methods have been developed, such as pseudo-inverse (PI), the principal component analysis (PCA) and even deep learning. In order to improve the reconstruction accuracy, research continues to come out. This paper proposes a method of spectral reflectance reconstruction based on the PCA and color objects classification using a color digital camera. The method is based on the three databases previously created, which are the spectral reflectance database of natural objects and their eigenvectors according to of color class of the objects, the spectral power distribution (SPD) of sunlight and its eigenvectors, and the camera's spectral sensitivity functions. The process of proposed method is as follows. Firstly, the camera takes an image of a natural scene containing a color card, and the SPD of the scene light is estimated through spectral reflectance of the color card. Secondly, each pixel of the image is classified to color class of objects according to its CIEXYZ value calculated through the camera's color characteristic data. Thirdly, spectral reflectance of each pixel is reconstructed according to the classes of the objects through eigenvectors of the spectral reflectance and sunlight. Finally, to prove the feasibility of the proposed method, an experiment of image color reproduction is carried out on the scene with plant leaf based on the estimated light sources and reconstructed spectral reflectance, and the results show that the accuracy of spectral reflectance reconstruction based on objects classification has been improved.
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