In order to meet the requirement of the global change study and overcome the defects in the prior art, the invention provides a low-spatial-resolution multisource remote sensing image space consistency correction method. The treating processes comprise the steps of (1) analyzing a low-resolution multisource remote sensing image to be processed, and obtaining the image and corresponding longitude and latitude data; (2) on the basis of the analyzed longitude and latitude data, adopting a scanning line geographic coordinate interpolation method to conduct geometric coarse positioning on an original image; (3) using an MODIS image as the standard to conduct image automatic matching, using a partitioning random sampling consistency method and an iteration least-squares method for removing mismatching points, and finally using a multiple-term formula for completing geometric fine correction; (4) using the MODIS image as the standard, conducting automatic matching on the corrected image and the MODIS image, obtaining a plurality of evenly-distributed control points to be used for calculating error in the image after the fine correction, and completing the automatic geometric correction precision evaluation.

Low-spatial-resolution multisource remote sensing image space consistency correction method

The invention discloses a method for rapid correction of image dithering. The method is used for rapid correction of image dithering during video hydrologic observation and comprises the following steps that firstly, a characteristic line of a reference image is extracted, and a reference line of the reference image is determined; secondly, a characteristic line of a real-time image is extracted; thirdly, the characteristic line of the real-time image is matched with the reference line, and a correction parameter is worked out; fourthly, the correction parameter is used for correcting image dithering. According to the method, the angle of 360 degrees by which a camera rotates is divided into a plurality of fixed angles, and each angle corresponds to a template segment set. During practical matching, the angle of the camera is pre-judged first, a corresponding application scenario is determined, and therefore the corresponding template segment set needed for follow-up matching is determined. Thus, through one-shot pre-judging, the application scenario corresponding to each rotation change of the camera can be converted into a fixed camera scenario, characteristic line matching operating time is shortened, the correction parameter is obtained rapidly, and rapid correction of image dithering is achieved.

Method for rapid correction of image dithering

The present invention provides a camera optical center rapid calibration method and apparatus. The method comprises: obtaining a distorted image; determining a plurality of suspected optical center coordinates within a preset range of the center of the distorted image; according to each suspected optical center coordinate and the constructed distortion model, correcting the distorted image to obtain a corrected image; respectively obtaining a straight line group in each corrected image; calculating the straightness of each of the straight line groups; and based on each straightness and the plurality of suspected optical center coordinates, calculating the optical center of a to-be-calibrated camera. Thus, only by obtaining a distorted image, the optical center of the camera can be calibrated, and the complexity of the calculation can be reduced by calculating the straightness.

Camera optical center rapid calibration method and apparatus

The invention discloses a remote sensing image registering method based on phase correlation and fractal dimension, and the method comprises the steps: employing a gradual change spatial filtering window for the spatial filtering of a reference image and a to-be-registered image, so as to reduce the impact from an edge, and guarantee that no new high-frequency components are introduced; carrying out the one-dimensional phase unwrapping of the left and right singular vectors obtained through the SVD of a normalization correlation matrix, and effectively intercepting the frequency components which are severely polluted according to the features of fractal dimension, thereby greatly improving the translation evaluation precision and success rate. The method is high in practicality, is suitable for registration of different-phase remote sensing images from different sources, is high in calculation efficiency, and is high in precision. Moreover, the calculation time is only correlated with the size of the image, and is independent of the complexity of contents.

Novel remote sensing image registering method based on phase correlation and fractal dimension

An image geometric correction method based on straight line segments has the advantages that straight line features in reference data can be fully utilized to allow geometric correction to be unlimited by feature point selection; the method is application to all imaging models and is universal; one to one correspondence of starting points and end points of straight line segments with the same name is not needed, and control data selection difficulty is lowered; the straight line segments are high in restraining conditions and correction precision; and multi-source data relates to registering of images of different time phases, different resolutions and different bands, a traditional grey-level-based image matching method is difficult to meet the requirements, and the straight line segments are easier to extract compared point features and influenced by resolution and grey level less easily

Image geometric correction method based on straight line segments

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Image geometric correction method based on straight line segments