Super-resolution reconstruction of ALOS-PRISM remote sensing images
ALOS-PRISM遥感影像超分辨率重建

We introduce the Advanced Land Observing Satellite and its Panchromatic Remote-sensing Instrument for Stereo Mapping (PRSIM)and use the super-resolution reconstruction approach to improve the resolution of thePRISM images. PRISM is a panchromatic radiometerwith2.5meter spatial resolution. PRISM instrumentbelongs to the class ofpush broom sensorand data are acquired by a linearCCDs array. PRISM product are processed into CEOS format for level1B1,1B2R, and1B2G.The image of Level1B2G is geometrically corrected data. The PRISM sensor can capture three images in the direction of looking forwards, downwards and backwards from the aircraft or satellite at same time. So we can obtain three images ofLevel1B2G in the same scene. Super-resolution technique can obtain a high-resolution image from observed multiple low-resolution images. The major advantage of the super-resolution approach is that itmay cost less and the existing low-resolution imaging systems can still be utilized. There is a greatneed to have fine spatial resolution datawith high fidelity and consistence in geo-referencing and intensity (tone)in the studies of land coverand land use, and their changes. In view of this, we present amaximum a posteriori estimation framework to obtain a high-resolution image from the PRSIM images ofLevel1B2G. This super-resolutionmethod is composed of twomain steps. In the first step, we presenta hybrid optical flow registrationmethod to dealwith the deformationwhich is brought by hypsography. In order to improve the registration accuracy of PRISM Level1B2G Images, we propose a new optical flow registrationmethod. This approach uses theNormalizedCross-Correlation registration algorithm beforewe useLucas-Kanade optical flow registration algorithm. Optical flow is the distribution of apparent velocities ofmovement of brightness patterns in an image. Optical flow can arise from relativemotion of objects and the viewer. The Lucas-Kanade registration approach divided the original image into smaller sections and assumes a constantvelocity in each section. Then itperforms aweighted least-square fitof the optical flow constraintequation. It can detectmost local distortions of PRISM image in sub-pixel accuracy, but thismethod may lead to somemisregister. The Normalized Cross-Correlation registration algorithm can reduce the misregister. So, we take the NCC registrationmethod to perform coarse registration firstly. The mixture registration method can remove the deformation which is broughtby hypsography in a greatmeasure. In this second step, to reconstruct the high-resolution image, we apply an iterative scheme based on alternative minimization to estimate the blur and HR image progressively. It is the combination of the blur identification and high resolution image reconstruction.We also improve the Gaussian PSF assumption mode,l and introduce the volatile blurs into the PSFmode.l By AlternatingM inimization (AM)algorithm, we can estimate the volatile blurs. Image qua