Research On Registration And Stitching Of Remote Sensing Image Of Agile Satellite

Remote sensing images which are always acquired from these devices on plane or satellite have been widely used in People’s daily life. We always hope to get high resolution and large size remote sensing images which can help to analysis of a region more deeply. However, it is contradiction between field of view and resolution in remote sensing field. The common solution is to acquire final image step by step. Firstly, we acquire small images which have high resolution. Secondly, we utilize registration and blending techniques to stitch two or more images which have overlapped areas into one large image. The registration and blending are very important techniques which can improve practical performance of the image. New agile satellites are appeared in recent years which have excellent acceleration ability. Agile satellite can aim and scan targets more accurately which make it meet the needs of complex tasks. However, due to its high mobility, agile satellites will make the imaging environment more complex, which affect the final quality of remote sensing images. We study the agile model of satellite deeply and in detail, quantified analysis the change of image quality after agile model.To do a research summary of a variety of registration techniques of images, such as Sequential Similarity Detection Algorithm (SSDA), Cross Correlation (CC) and Fourier transform. The Scale space can help to Understanding of scale-invariant principle. Scale Invariant Feature Transform (SIFT) and Harris corner extraction are very famous methods which can lay a solid foundation for registration and stitching of remote sensing images.Agile satellites have good mobile abilities which can get more different parts of the image. We firstly build geometric model, radiation model, atmospheric model and camera model, secondly propose some principals:Signal to Noise Ratio (SNR), MTF, Gradient Information Entropy, Affine degeneration and Structure Similarity Parameter to evaluate the quality of the image acquired from agile satellites. Our models can evaluate the quality before the target which can help users to make a decision of the target.Image restoration and Image fusion can help to reduce image fuzzy and recover the color information of image. Field of Experts restoration method can improve the quality of blurring image, which can make blurring image be used in some situation where is very strict about the quality of the image. The restoration image can extract more feature points than blurring image, and the better matching precision will make the registration more accurate. Image fusion can combine panchromatic image and multispectral image which make the fusion image has high resolution and rich color information.We have proposed three improved registration methods of remote sensing images, which can help to acquire better adaption ability in practical application. Firstly, registration method based on human vision introduced the technology of feature point weights. Gradient information of image is the judgment standard which can realize registration of image automatically. The combined stitching method can make the transition of overlap of image more naturally. The double-feature registration method employs two feature extractions, SIFT and Harris. Harris can extract corner features more accurate in urban images, however bad ability in the regions such as water and grassland. After comprehensive comparison, we utilize the SIFT to extract feature from flat area, and Harris to extract feature from urban area. This combined method can acquire more accurate result. Registration based on refined control points can reduce affection of the wrong match of feature points. Remote sensing images always have a lot of objects, so we can extract a mass of feature points which however will also increase the rate of wrong matching. In fact, we only need a few points to resolve the transformation between two or more images. This method can help us to find five groups of well-matched feature points, which make the registration easier.