The stripe noise is a key factor that affects imaging quality of satellite multi-hyperspectral remote sensing images, which also has a serious effect on the interpretation and information extraction of remote sensing images. Complex surface textures mixed with strip noises in the high-resolution multi-spectral remote sensing of satellite are extremely difficult to remove, this paper analyzes the Markov random field prior model method, combines the Huber function to propose a universal, fast and effective Huber Markov destriping method. According to the statistical characteristics of the image gray level variation, the distribution features and mutual relationship between each pixel and its neighborhood pixels in the image, the co-occurrence matrix reflecting the contrast gray characteristics of the image is connected with the threshold T of Huber function, which is automatically iteratively determined during the noise removal process, and will be able to remove image noises as well as preserving its edges and details effectively. In order to solve the time complexity of the algorithm caused by the pixel space information introduced by the Huber Markov random field algorithm, the GPU adaptive partitioning technique is adopted to accelerate the algorithm. Experimental results show that the destriping method based on Huber function Markov random field can remove the strip noise effectively, while preserving texture details of the image, which can be applied to a variety of noise-containing images. Meanwhile, GPUbased adaptive partitioning technology has been adopted, which has greatly improved the computational efficiency of processsing massive remote sensing images, and lays a foundation for the application of remote sensing satellite images in China.
The effects of the ionosphere on spaceborne synthetic aperture radar (SAR) systems have received attention since the development of ALOS PALSAR (L-band). One of them is the Faraday rotation (FR) due to the dispersive nature of ionosphere and the existence of Earth’s magnetic field. The FR error is obviously embedded in polarimetric data of PALSAR systems, which destroys the scattering matrix. Nevertheless, distorted echoes contain abundant ionospheric information, the ionospheric sounding based on the scattering matrix data can become possible if the mechanisms of ionospheric interference can be understood and accurately modeled. SAR systems are generally characterized by high spatial resolution, this powerful technique can detect kilometer-scale ionospheric information where such unprecedented spatial resolution was previously inaccessible (e.g., such resolution is 1 to 2 orders of magnitude higher than that obtained by GPS). In this paper, by using the ALOS PALSAR full-polarization data sets, we quantitatively evaluate the reliability and accuracy of retrieved TEC information. We have used the observation results of incoherent scattering radar (ISR) to verify the accuracy of our results, given that ISR is currently the most powerful ground means for ionospheric monitoring and the ideal means for ionospheric data verification. In our work, the AMISR data for the Alaskan region in the United States is selected. After screening the data, we have selected and compared three sets of SAR and ISR results obtained at the same observation time (universal time: 8/6/2010, 21:6:25; 3/19/2011, 7:32:50; 3/31/2011, 7:28:16) and place. Our results show that the deviation between the results of SAR and ISR is only 0.1-0.35 TECU accounting for different factors, such as system and geographical deviations. However, the accuracy of the most widely used GPS data can only up to 1-2 TECU. Both accuracy and resolution of ionospheric sounding using fullpolarization SAR are therefore superior to those of ionospheric sounding using GPS.
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