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Fluorescent molecules play an important role in many fields due to their characteristic of high sensitivity and easy operation. The traditional method for detecting fluorescent molecules is Laser Scanning Confocal Microscopy, but it has light pollution and can only detect a single class of fluorescent molecules at once. Recently, Microscopic Hyperspectral Imaging(MHSI) technology has been used to the detection of fluorescent molecules due to its high spectral resolution and non-destructive detection. However, the low spatial resolution of MHSI makes it difficult to conduct high-precision molecular research. Therefore, it is important to develop an image processing method to improve it. In this work, a twostep data processing method was proposed to enhance automatic classification effect of fluorescent molecules. We used a microscopic hyperspectral system to image the mixed five kinds of fluorescent molecular samples in transmission mode. The first step is based on the difference in unit slope of spectra curve (wavelength range was chosen from 410 nm to 550 nm) between fluorescence molecule and the background, and an image segmentation method based on minimum light transmission point is proposed. The second step is to calculate the relative absorbance of each voxel according to the nearest background voxel found on the basis of image segmentation, and to take the absorbance as the final classification feature. Compared with the traditional transmittance feature on six kinds of machine learning classification models, the average classification accuracy of the new features can be improved by 2.2%, and the time consumption per classification can be reduced by 1/3 approximately. In conclusion, the proposed two-step data processing method is suitable for the classification of multi-kinds of fluorescent molecules, which has the advantages of high efficiency and accuracy, and is expected to be widely used in biology, medicine, materials and other fields.
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