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 the PRISM images.PRISM is a panchromatic radiometer with 2.5 meter spatial resolution.PRISM instrument belongs to the class of push broom sensor and data are acquired by a linear CCDs array.PRISM product are processed into CEOS format for level 1B1

1B2R

and 1B2G.The image of Level 1B2G 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 of Level 1B2G 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 it may cost less and the existing low-resolution imaging systems can still be utilized.There is a great need to have fine spatial resolution data with high fidelity and consistence in geo-referencing and intensity（tone）in the studies of land cover and land use

and their changes.In view of this

we present a maximum a posteriori estimation framework to obtain a high-resolution image from the PRSIM images of Level 1B2G.This super-resolution method is composed of two main steps.In the first step

we present a hybrid optical flow registration method to deal with the deformation which is brought by hypsography.In order to improve the registration accuracy of PRISM Level 1B2G Images

we propose a new optical flow registration method.This approach uses the Normalized Cross-Correlation registration algorithm before we use Lucas-Kanade optical flow registration algorithm.Optical flow is the distribution of apparent velocities of movement of brightness patterns in an image.Optical flow can arise from relative motion of objects and the viewer.The Lucas-Kanade registration approach divided the original image into smaller sections and assumes a constant velocity in each section.Then it performs a weighted least-square fit of the optical flow constraint equation.It can detect most local distortions of PRISM image in sub-pixel accuracy

but this method may lead to some misregister.The Normalized Cross-Correlation registration algorithm can reduce the misregister.So

we take the NCC registration method to perform coarse registration firstly.The mixture registration method can remove the deformation which is brought by hypsography in a great measure.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 model

and introduce the volatile blurs into the PSF model.By Alternating Minimization（AM）algorithm

we can estimate the volatile blurs.Image quality assessment plays an important role in image super-resolution reconstruction.Peak Signal-to-Noise Ratio（PSNR）and Mean Squared Error（MSE）are the most widely used objective image quality indexes.The two indexes are Full-Reference image quality assessment.Unfortunately

we can not obtain the original high resolution image in the super-resolution reconstruction process.So we propose two noreference image quality assessments which are entropy and Mean Grads.Experimental results show that our super-resolution method is effective in performing blind SR image reconstruction with PRISM images and our super-resolution reconstruction algorithm has better performance in edge preserving than bicubic interpretation.The resolution of PRISM image is enhanced effectively.The enhancement show that the mixture registration method can reach sub-pixel precise and the modification of the Gaussian PSF assumption model correspond to the actual PSF of PRISM images.The AM blind super-resolution approach can be used to enhance the resolution of aerial and remotely sensedimages.