Evaluation of Control Points’ Distribution on Distortions and Geometric Transformations for Aerial Images Rectification (original) (raw)
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Pre-processing Algorithm for Rectification of Geometric Distortions in Satellite Images
Defence Science Journal, 2011
A number of algorithms have been reported to process and remove geometric distortions in satellite images. Ortho-correction, geometric error correction, radiometric error removal, etc are a few important examples. These algorithm require supplementary meta-information of the satellite images such as ground control points and correspondence, sensor orientation details, elevation profile of the terrain, etc to establish corresponding transformations. In this paper, a pre-processing algorithm has been proposed which removes systematic distortions of a satellite image and thereby removes the blank portion of the image. It is an input-to-output mapping of image pixels, where the transformation computes the coordinate of each output pixel corresponding to the input pixel of an image. The transformation is established by the exact amount of scaling, rotation and translation needed for each pixel in the input image so that the distortion induced during the recording stage is corrected.
International Journal of Digital Content Technology and its Applications, 2012
Rectification process is necessary so as to orient a satellite imagery to a planar surface and make its geometry planimetric. It establishes the image in the correct spatial location and orientation for subsequent quantitative analysis. This paper introduces the rectification of a true color Satellite imagery that was synthesized out of a Quick Bird high resolution Satellite imagery data for quantitative analysis. However an overview of the geometric correction process was also highlighted. Reference system is an important factor in order to verify and identify all measurements and data collection processes from Satellite imageries. The quality of the outputs depends on how good and refined the reference system is defined. The technique uses Global Positioning System (GPS) positioning information, geo-reference and other parameters, such as interpolation method to automatically register and ortho rectify the raw imagery data. Through the process, a seamless imagery is produced. Through semi-automatic and manual editing, a standard satellite-map imagery that satisfies geometric extraction needs was produced which would subsequently be used to determine quantitatively, geometric cadastral boundaries, hence, analyzing the imagery data. The result of the satisfactory imagery rectification shows that the total root mean square (RMS) error is 0.6152 meter, for X is 0.4119 meter and for Y is 0.4570 meter.
Geometric Correction for Very High Resolution Satellite Images
Very high resolution satellite images, such as World view2, are important for produce new maps or update existing ones. Maps are useful for planning and navigation purposes in any country. Main aim of this work is the assessment of a methodology to achieve the best possible geometric accuracy in rectified imagery products obtained from World view2. The research includes, studying the effect of the number of Ground Control Points (GCPs) with various 2D polynomial rectification models on the accuracy geometric correction step. The resulting RMSE on the check points (CPs) will be evaluated. Results showed that using a second order polynomial for world view 2 image rectification with 33 well distributed GCPs resulted in a total RMSE 0.96 m for a number of 13 CPs which is less than the value of two pixel size of the used satellite image. Also, by using from 20 GCPs till 33, the accuracy of geometric correction for all polynomial orders has a small variation.
Islamic University, 2016
ABSTRACT Every satellite image has some distortions that affect the geometric accuracy of these images, These distortions are classed in two groups; systematic and non-systematic distortions. The systematic distortions are corrected by applying formulas derived by modeling the source of the distortions mathematically. Non-systematic distortions, and residual unknown systematic distortions are corrected by analysis of well-distributed ground control points (GCPs) which occurring on the satellite image. The satellite images are delivered after applying some correction processes like radiometric- distortions, but some of these images must be rectified to remove the other types of distortions, so rectification processes are applied. To apply the rectification processes, it is necessary to acquire the fundamental model of the satellite and applying different mathematical models. These models relate between the point on the image and its conjugate on the earth, and using several models to give the best accuracy. For this research, a satellite image for Gaza City-Palestine was used. These 38 GCPs are collected and read which are divided to 26 control points for the rectification process and 12 control points as check points to assure the accuracy of the process. Erdas Imgaine Program was used as software at the rectification and analysis process. In general, it is found that the third order polynomial gives the best results. The effect of GCPs number on resulted accuracy was studied. The rectification operation is repeated by reducing the number of the GCPs gradually in the image. It is found that the accuracy of rectification is directly proportional with the number of GCPS.
Robust rectification of aerial photographs in an open source environment
Computers & …, 2011
Aerial photographs provide the basis for developing indices of landscape composition and structure as sensitive measures of large-scale environmental change over relatively long periods of time. In view of this, proper image rectification is needed to enable geometrically unbiased application of landscape metrics in order to obtain meaningful results. It is also particularly important to provide researchers with image rectification tools within an open source environment, in order to: (i) guarantee free and robust tools for processing remote sensing data, (ii) facilitate customization, and (iii) provide useful support via forums and email lists. In this paper we provide a complete description of a robust and freely licensed toolchain for orthorectifying images, which is available in the open source software GRASS GIS. We will first sketch the theoretical background behind rectification and then illustrate the workflow of the orthorectification procedure in GRASS GIS. Highlights ► Aerial photos can be used for studying environmental change over long time periods. ► Proper image rectification is needed to enable geometrically unbiased results. ► An Open Source environment guarantees free and robust processing of such data. ► We describe a freely licensed toolchain for orthorectifying images into GRASS GIS. Keywords: Aerial photographs; Free open source software; Geometric correction; GRASS GIS; Orthorectification; Remote sensing Abbreviations: FOSS, Free open source software; GPL, general public license; GRASS, Geographical Resources Analysis Support System; OSGeo, Open Source Geospatial Foundation
Geometric correction is an important process for producing georeferenced remote sensing images that are used for many applications as map production, feature measurements, change detection and object tracking. Geometric correction is a post processing process that is applied on captured remote sensing images for correcting images against sensor and/or environmental error sources. There are different geometric correction techniques depending on an algorithm, a source of distortion and nature of available data. The available data is a satellite image for an area of study without information about its coordinates, projection or source of distortion. Ground Control Points (GCPs) are available for the same area of study. Geometric correction process for image of study area is applied many times based on different number of available GCPs and based on different mathematical model approaches. The objective of this research paper is to get the suitable requirements for geometric correction for satellite images using Ground Control Points (GCPs). The research investigated the optimum requirements for an input satellite image using CGPs. Assessment based on Root Mean Square (RMS) values is used to estimate the accuracy of resultant images. Theminimum and optimum requirements are achieved when using 4 GCPs with specific distribution. The geometric correction gave accurate results when using two dimensional coordinate transformations with first order degree of polynomial with of minimum number of GCPs.
ISPRS Congress, 2004
For various satellite imagery applications, geo-referencing through rectification is a common operation. Rigorous mathematical models with the aid of satellite ephemeris data can present the relationship between the image space and object space. With government funded satellites, access to calibration and ephemeris data allowed the development of these models. However, for commercial high-resolution satellites, these data have been withheld from users, and therefore alternative empirical rectification models have been developed. In general, most of these models are based on the use of control points. The lack of control points in some remote areas such as deserts, forests and mountainous areas provides a catalyst for the development of algorithms based on other image features. One of the alternatives is to use linear features obtained from scanning/digitizing hardcopy maps, from terrestrial mobile mapping systems or from digital images. In this work, a new model named the Line Based Transformation Model (LBTM) is established for satellite imagery rectification. The model has the flexibility to either solely use linear features or use linear features and a number of control points to define the image transformation parameters. As with other empirical models, the LBTM does not require any sensor calibration or satellite ephemeris data. The underlying principle of the model is that the relationship between line segments of straight lines in the image space and the object space can be expressed by affine or conformal relationships. Synthetic as well as real data have been used to check the validity and fidelity of the model, and the results show that the LBTM performs to a level comparable with existing point based transformation models.
Georeferencing satellite images is an essential procedure to carry out most remote sensing applications. The quality of this process will affect all the ulterior procedures and products. Independent test ground control points (GCPs) are required to assess the quality of the correction. However, a representative number is hardly obtained when they are manually located. This work studies the effect of the number of GCPs in the geometric correction quality when they are manually located. The methodology has been applied to Landsat TM images in a region with complex relief (heights ranging from 0 to 3000+ m). The work presents a spatial representation of the error and discusses its role in the visualisation of the quality. Moreover, we critically discuss the usage of indicators as the RMS error without considering the number of GCPs or the method used in their placement in the realistic assessment of the geometric quality of the imagery. Indeed, it is shown that, for the studied scenes, a minimum of 25 GCPs is needed to achieve a test RMS smaller than a pixel and that not using independent GCPs leads to unrealistic quality indicators. Moreover, manual placement of GCPs gives clearly worst results than automatic procedures.
Continuous piecewise geometric rectification for airborne multispectral scanner imagery
Geometric rectification of airborne multispectral scanner image data using traditional polynomial functions often cannot provide satisfactory RMSE accuracy due to the complex nature of geometric distortions in the data. The discrete approach to rectifying these data generates segmented pieces that may cause edge-matching problems after they are reassembled. To improve the rectification accuracy while retaining the continuity of the rectified strip, a continuous piecewise geometric rectification approach is introduced. Using logical divisions of a strip and the concept of overlapping area anchor ground control points, the approach localizes the complex distortion and greatly improves the edge-match between pieces. A description of the procedure is presented along with two case studies, each having a diffrent set of sensor and terrain characteristics, to illustrate the advantages of this approach versus traditional techniques.