Research And System Implementation Of Remote Sensing Image Noise Evaluation Method

In recent years,with the improvement of remote sensing technologies,remote sensing image has become one of the most important tools to observe information of the surface of earth.Hyperspectral remote sensing images(HSI)contain abundant spectral information and large coverage area which are researched in by many researchers.However,hyperspectral remote sensing images are easily corrupted by noise because of the less energy of spectral band.Accurate approximation of noise level can provide prior parameters for image processing algorithms.Many key technologies of hyperspectral remote sensing images are studied and a new noise estimation method is proposed.Our work mainly includes the following parts:(1)To accurately discriminate spectral similarity,we proposed a wavelet transforming spectral pan-similarity measure(W-SPM).The experiment confirms our noisy robustmetric can classify accurately.(2)An isotropic homogeneous region detection algorithm(IHRDA)is proposed which uses W-SPM.The new algorithm utilizes two steps of detection to make pixels similar in all directions and successfully avoid false detection and over-detection.(3)A method is proposed to search the optical region and estimate the image noise level represented by the residual standard deviation of the optical region.In experiments,the noisy synthetic images and true hyperspectral images are used to compare our method with many existing methods,which demonstrate our method is accurate and stable.Finally we analyze our method’s influence over other image processing algorithms.The experiment compares our method with other methods and demonstrates that our method can achieve better denoising performance by providing accurate prior parameter.(4)Combining these theoretical research results,we program a software to evaluate hyperspectral remote sensing images which contains a lot of technologies mentioned above and compares many methods quantitatively.