Abstract: A method for performing point-to-point whiteboard parameter ratio correction on a hyperspectral image includes: capturing hyperspectral data of a standard reference whiteboard in advance as white(x,y,w), and storing the records, then captures hyperspectral data of a sample as sample(x,y,w); then selecting an unobstructed and unshaded whiteboard area within a certain range of the hyperspectral data sample(x,y,w) of the test sample, and labeling the area as Area A; calculating a spectral average of the ROI on the sample image as SA(w); calculating a spectral average of whiteboard data in the same position as the ROI as WA(w); dividing the two spectral averages, and obtaining a correction coefficient alpha(w)=WA(w)./SA(w); multiplying the whiteboard parameter ratio correction coefficient alpha(w) by a sample reflectance image matrix after whiteboard parameter ratio correction to obtain a final hyperspectral reflectance image matrix REFL(x,y,w)=alpha(w).*sample(x,y,w)./white(x,y,w).

Abstract: A method for extracting spectral information of a substance under test includes: identifying a pixel region A(x, y) occupied by an object under test from a hyperspectral image acquired; extracting a specular reflection region Aq and a diffuse reflection region Ar from the pixel region A(x, y), and calculating a representative spectrum Iq(?) of the specular reflection region Aq and a representative spectrum Ir(?) of the diffuse reflection region Ar, respectively; by comparing each element in the representative spectrum Iq(?) of the specular reflection region Aq with each element in the representative spectrum Ir(?) of the diffuse reflection region Ar, separating information of a light source from spectral information of the object to obtain a first spectral invariant C(?). This method does not require additional spectral information of the light source, which improves the analysis efficiency.

Abstract: A method and device for restoring and reconstructing a light source spectrum based on a hyperspectral image are provided. A hyperspectral image is processed to obtain a first maximum spectrum. The first maximum spectrum is matched with a light source spectral basis vector set in a light source spectrum dictionary, and a linear decomposition is performed to obtain a dictionary set. The maximum value of each waveband is obtained and iterative approximation is performed. Therefore, it can quickly restore the spectrum of the light source of the shooting environment from a single hyperspectral image using a relatively simple calculation process, and can still achieve a good restoration effect for monochromatic image scenes or image scenes with few colors, even close to the real light source spectrum. After the light source spectrum is obtained, this information can be further utilized for different kinds of applications.

Abstract: A method and device for restoring and reconstructing a light source spectrum based on a hyperspectral image are provided. A hyperspectral image is processed to obtain a first maximum spectrum. The first maximum spectrum is matched with a light source spectral basis vector set in a light source spectrum dictionary, and a linear decomposition is performed to obtain a dictionary set. The maximum value of each waveband is obtained and iterative approximation is performed. Therefore, it can quickly restore the spectrum of the light source of the shooting environment from a single hyperspectral image using a relatively simple calculation process, and can still achieve a good restoration effect for monochromatic image scenes or image scenes with few colors, even close to the real light source spectrum. After the light source spectrum is obtained, this information can be further utilized for different kinds of applications.

Abstract: A method for extracting spectral information of a substance under test includes: identifying a pixel region A(x, y) occupied by an object under test from a hyperspectral image acquired; extracting a specular reflection region Aq and a diffuse reflection region Ar from the pixel region A(x, y), and calculating a representative spectrum Iq(?) of the specular reflection region Aq and a representative spectrum Ir(?) of the diffuse reflection region Ar, respectively; by comparing each element in the representative spectrum Iq(?) of the specular reflection region Aq with each element in the representative spectrum Ir(?) of the diffuse reflection region Ar, separating information of a light source from spectral information of the object to obtain a first spectral invariant C(?). This method does not require additional spectral information of the light source, which improves the analysis efficiency.