Mixed Noise Estimation for Hyperspectral Image Based on Multiple Bands Prediction

Abstract

The noise in hyperspectral images (HSIs) is mixed with signal-independent (SI) thermal noise and signal-dependent (SD) photon noise. In this letter, a mixed noise estimation algorithm is proposed to estimate the noise level of SI and SD noise, respectively. The proposed algorithm removes the spectral correlation of the HSI by multiple linear regression (MLR) on several neighboring bands. According to the relationship between the prediction coefficients by MLR and the local statistics, an overdetermined equation system with respect to the variances of the SI and SD noise is established. Then the estimated noise variances are solved by the least-squares (LS) method. To ensure the accuracy of local statistics, regional clustering-based spatial preprocessing (RCSP) for HSI superpixel segmentation is employed to detect the homogeneous region. Experimental results on the simulated data confirm considerable improvements, and the real-life data experiment is implemented to validate the performance of the proposed algorithm.