Reflectance Calibration with Normalization Correction in Hyperspectral Imaging

Today, hyperspectral (HS) imaging has become a powerful tool to identify remotely the composition of an interest area through the joint acquisition of spatial and spectral information. However, like in most imaging techniques, unwanted effects may occur during data acquisition, such as noise, changes in light intensity, temperature differences, or optical variations. In HS imaging, these problems can be attenuated using a reflectance calibration stage and optical filtering. Nevertheless, optical filtering might induce some distortion that could complicate the posterior image processing stage. In this work, we present a new proposal for reflectance calibration that compensates for optical alterations during the acquisition of an HS image. The proposed methodology was evaluated on an HS image of synthetic squares of various materials with specific spectral responses. The results of our proposal show high performance in two classification tests using the K-means algorithm with 97% and 88% accuracy; in comparison with the standard reflectance calibration from the literature that obtained 77% and 64% accuracy. These results illustrate the performance gain of the proposed formulation, which besides maintaining the characteristic features of the compounds within the HS image, keeps the resulting reflectance into fixed lower and upper bounds, which avoids a post-calibration normalization step.

Automated summary

Hyperspectral imaging is a powerful tool for remotely identifying the composition of an area of interest. However, unwanted effects during data acquisition can complicate the image processing stage. This study proposes a new reflectance calibration method that compensates for optical alterations and achieves high accuracy in classification tests.