Hyperspectral Band Quality Analysis Based On Dictionary Representation

Hyperspectral image can acquire hundreds of bands with wavelengths ranging from visible spectrum to infrared, and the rich discriminant information has led to the widespread applications in the military and civil fields. However, due to the influence of imaging devices, some bands are polluted by noise, which bring great inconvenience to subsequent processing. Therefore, in order to quickly and accurately find low quality and noisy bands in hyperspectral image, we propose a hyperspectral image quality analysis method based on band dictionary representation. Firstly, a representative band dictionary is constructed to accurately represent the dominant information in hyperspectral image. Then, the band subset dictionary is used to reconstruct all the remaining bands in the hyperspectral image and obtain the reconstruction coefficients of each band. Finally, representation error is calculated to analyze the quality of each band. By comparing the representation errors of the bands, the quality of each band can be estimated. Experimental results on three real-world hyperspectral images demonstrate the proposed method can effectively and quickly select low quality bands and noisy bands without any priors.