Image registration aims to establish precise geometrically alignment between two images of the same scene taken at different times, different viewpoints, or different sensors. It is an essential task in diverse remote sensing and photogrammetry processes such as change detection, 3D modeling, information fusion, geometric correction, and bundle block adjustment. Nowadays, feature based approaches are generally used for satellite image registration due to their resistant against geometric and radiometric variations. A feature based image registration method comprises three main steps: (1) control point (tie-point) extraction, (2) transformation model computation, and (3) image resampling. In the first step, some distinctive conjugate features are automatically extracted using various image matching approaches. In the second step, a suitable transformation model between two images is computed using the extracted control points from the previous step. In the third step, the input image is rectified to the geometry of the base image using the computed transformation model.
Transformation models generate the spatial relation between the images and play a critical role in the positional accuracy of the image registration process. Various transformation models have been proposed for remote sensing image registration. The transformation models are generally divided into two types: (a) global models and (b) adaptive models. The global models have a constant number of parameters and describe the global spatial relations between two images. In contrast, the number of parameters in adaptive models are not constant and varies with the severity of the geometric variations and the number of control points. Each transformation model has its strengths and drawbacks. In this paper the capability of some popular transformation models, including similarity, projective, polynomials of degrees 1 to 4, piecewise linear (PL), Multiquaderic (MQ) and Pointwise (PW) are evaluated for high resolution stereo satellite imagery.
To extract high accurate and well distributed control points, an integrated image matching process based on FAST (Features from Accelerated Segment Test) detector, SIFT (Scale Invariant Feature Transform) descriptor and the least square matching algorithm has been proposed. In this method, the initial point features are efficiently extracted using FAST algorithm in a gridding strategy. Then, the well-known SIFT descriptors are computed for extracted features. The control points are determined using feature descriptor comparison in two images, and their positional accuracy are improved using least square matching method.
Two evaluation criteria, including computation speed and positional accuracy are used to investigate the capability of the transformation models. To investigate the effect of the feature density in quality of the transformation models, the extracted control points are divided into four classes with different number and distribution of the control points. The experimental results using two high resolution remote sensing image pairs from ZY3 and IKONOS sensors, show that the adaptive MQ model provides the best results, followed by PW and PL models. In contrast, the similarity, projective and global polynomial models do not provide acceptable results for accurate registration in high resolution remote sensing images. However, the commutation time in adaptive models especially in MQ is very high. The registration accuracy of the proposed approach for the MQ model is 2 and 1.9 pixel, for the ZY3 and IKONOS images respectively

Since there are different applications of radar in various sciences doing research and study on radar is necessary. one of the radar image applications is DEM generation. one of the essential DEM generation steps is image matching. radar image matching is more complex than matching on optical images. one of the most serious problems in SAR image matching is speckle noise. besides the noises, some geometric distortions like shadows, foreshortening, and layover also increase the complexity images matching. in this paper, a SAR image is firstly filtered by a speckle suppression algorithm and then a Harris algorithm is used to extract feature points. Then descriptor is introduced and local matching is performed. Furthermore,we compute the first image point extracted to the second image point extracted displacement with multi quadric equation. Finally, we use least square image matching for image matching improvement. Proposed method for on TerraSAR-X images of the area JAM-IRAN was tested. The result shows that our proposed method is superior to the Local matching in terms of accuracy and number of matched points.

Due to the wide applications of hyperspectral images, economical and innovative imaging systems are developed to acquire such images. In order to use hyperspectral images, it is necessary to establish an accurate relation between the ground space and the image space, which needs numerous Ground Control Points (GCPs). This fact highlights the need for developing geometric corrections methods for any camera design. BaySpec OCI-F (400-1000 nm) is one of the innovative cameras that acquires pushbroom hyperspectral images. In addition to the pushbroom sensor, the camera uses a frame sensor that acquires images at the same time as the pushbroom sensor and with the same temporal rate. In this article, a geometric correction method for pushbroom images of OCI-F camera is proposed. Based on the camera’s imaging design, the first step of the method determines a set of calibration parameters which geometrically relates the pushbroom and the frame sensors. Then using this relation and the geometric relations among consecutive frames, the pixels of the pushbroom scene are rearranged and form the corrected image. The proposed method determines the relation among the consecutive images via Least Square Matching (LSM) method. The results show that the correction method has decreased the geometric distortions of the raw pushbroom scene by 62.2% on average. Such a reduction causes the average accuracies of two-dimensional and three-dimensional generic models which relate image space and ground space together, to increase by 34.1% and 39.9% respectively.

Having a proper Digital Surface Model (DSM) is crucial in many earth dependent applications such as change detection, 3D urban modelling, urban planning and environmental monitoring. Generating DSM from High resolution stereo satellite images provides good possibilities in this issue. Image matching technique has an important role in DSM generation from stereo satellite images and directly effects on the quality of DSM. Up to now, many image matching algorithms have been proposed, such as Least Square Matching (LSM), Dynamic Programing (DP) and Semi-Global Matching (SGM) which the last one has a higher efficiency than the other methods.
One of the main inputs for SGM algorithm are the epipolar images. Epipolar images are rectified images so that each row at left image corresponds with the same raw at right image (i.e. parallax y is zero). It will cause to accelerate the matching process and reduce the search space from 2D into 1D. Unlike the images with perspective geometry (Frame Camera), the epipolar geometry of linear array images could not modeled with straight line. Therefore, generating the epipolar images from high resolution stereo satellite images has been a research topic in the photogrammetry and remote sensing.
Among the literatures, one can find different epipolar resampling methods proposed in image and object space. All these methods need Rational Polynomial Coefficient (RPC), orientation parameters or ground control points (GCP). Unfortunately, the orientation parameters are not available and RPCs need to be modified. Also measuring the GCPs is not affordable. So it is significant to decrease or completely remove the need for these information in epipolar resampling.
In this paper, we aim to propose a method to build epipolar image by modeling the epipolar curves without any extra information. In the proposed method, dividing the original image into overlapping tiles and utilizing computer vision algorithms to automatically find the corresponding points in stereo images are employed.
Our proposed framework consists of four main steps. The first step is pre-processing of data. In this step, the stereo pair is automatically registered and divided into overlapping tiles. To register images in the absence of RPC, GCPs or other metadata, SURF feature detector and RANSAC algorithm are employed. SURF operator automatically identifies point features in the images. Then RANSAC algorithm filters wrongly detected conjugate points among all points. These matched points will be used to register image pair using an affine transformation.
In the second step, all registered stereo image tiles will be epipolarly resampled. Epipolar geometry for the image tiles of satellite images is equivalent to simple line. Fundamental matrix and Morgan’s method are used to build epipolar images.
In the third step, disparity maps of corresponding epipolar images are computed using Semi-Global matching (SGM). The SGM algorithm needs large amount of temporary memory for saving matching costs cube and aggregated costs cube. The size of temporary memory depends on the image size and the disparity range. The solution which has been proposed by SGM is to divide the epipolar images into small image tiles. This idea has also followed in our proposed method.
Finally in the fourth step, the result of experiments will be evaluated. The mean and standard deviation of y-parallax for some conjugate points is used to compare the result of different methods.
A stereo-pair acquired by GeoEye-1 high resolution satellite pushbroom sensor is used in our experiments. This image scene includes urban areas over Qom city in Iran. In continue, epipolar images are produced using fundamental matrix and Morgan’s method. The mean and standard deviation of y-parallax is computed for these methods. The results show that our proposed method without using RPCs could produce the epipolar images with sub-pixel accuracy.

This article aims to investigate the stability and estimate the aerodynamic coefficients of an aircraft based on experimental flight data. The least squares method was used to estimate these coefficients by using data from an inertial measurement unit (IMU) and global positioning system (GPS). This research, for the first time, has estimated these coefficients on a high-wing aircraft and during various flight maneuvers, including takeoff, landing and variant turns. It has been conducted utilizing time and frequency response data based on actual flight. In this article, the match between the recorded flight path of the aircraft and the flight path plotted by MATLAB software output was evaluated, and the output of the flight path model confirmed this match. Moreover, to improve the estimation of aerodynamic coefficients, the least squares method was modified using past inputs to estimate a value. The method explained in this article was evaluated in 5 experiments using actual flight data from the aircraft. Finally, the maximum estimation error generated by the modified method is 6.7%, which was related to the pitching moment coefficient.

History matching is still one of the main challenging parts of reservoir study especially in giant brown oil fields with lots of wells. It would be a challenge in reservoir engineering that due to various parameters and uncertainties in study of reservoirs, many simulation runs are needed to reach a good match for responses in conventional mechanism of history matching. However, for accelerating history matching part, new methods, which are called as assisted or automated history matching (AHM), have been established. In this paper, the latest approach for automated history matching (AHM) has been applied in a real brown field containing 14 wells with multiple responses that is located in south of Iran. Least square support vector machine (LSSVM) has been applied to create proxy model based on cubic centered face method. The optimization algorithms, used in this research, consist of genetic algorithm (GA) and particle swarm optimization (PSO).  Introduction In the latest studies in geosciences and reservoir characterization, employing a proxy model that acts faster, instead of real reservoir model, has led to good results. One of the most important sections in fulfilled study (FFS) and master development plan is history matching, which plays an important role in production scenarios and future production plan of the under study reservoir. In this paper, one of the newest methods is used for making proxy model and then, the model for history matching is optimized.  Methodology and Approaches Least square support vector machine (LSSVM) has been employed to create proxy model based on cubic centered face (CCF) method. The optimization algorithms of genetic algorithm (GA) and particle swarm optimization (PSO) have been used in this research.  Results and Conclusions A new proxy model has been successfully constructed using 1086 samples leading into determination coefficient (R2)

The aim of the present study was to investigate and compare the performance of various color matching procedures inclusive of color matching based on Allen''s rough match and an iterative procedure using the CIELAB color difference formula، spectrophotometric procedures such as McGinnis least square method، iterative method، weighted iterative method and spectrophotometric color matching based on fundamental color stimulus. To this end، 165 samples were dyed. Initially، the comparisons were made theoretically on the basis of color differences and indices of metamerism. Additionally، such comparison was extended practically by dyeing 24 real dyed samples. Practical results were in good agreement with the theoretical results، showing a relatively better performance of the spectrophotometic color matching procedure based on the fundamental color stimulus. J. Color Sci. Tech. 7 (2013)، 75-83© Institute for Color Science and Technology.

In this paper, a new robust approach based on Least Square Support Vector Machine (LSSVM) as a proxy model is used for an automatic fractured reservoir history matching. The proxy model is made to model the history match objective function (mismatch values) based on the history data of the field. This model is then used to minimize the objective function through Particle Swarm Optimization (PSO) and Imperialist Competitive Algorithm (ICA). This procedure leads to matching of history of the field in which a set of reservoir parameters is used. The final sets of parameters are then applied for the full simulation model to validate the technique. The obtained results showed that due to high speed and need for little data sets, LSSVM is the best tool to build a proxy model. Also the comparison of PSO and ICA showed that PSO is less time-consuming and more effective.

With the rapid development of spatial data production, collection, and processing techniques large amounts of data, which cover the same geographical extent, are available. These huge amounts of data motivate the researchers in geospatial analysis domain to create multiscale database in order to update the datasets and perform the multi-scale analysis. In the core of any spatial multiscale database is the data matching process. In this paper, a new algorithm is proposed for data matching which is implemented and evaluated in two major steps. Firstly, after pre-processing, the search and identification of candidate objects in the two datasets are carried out based on the topological features. Secondly, the best pair of corresponding objects is selected using the least squares method and comparing the geometric features of linear objects between the candidate object selected in the previous step. The algorithm is evaluated on the roads maps of scales1:25000 and1:50000 produced by national cartography center of Iran (NCC).The results show that the algorithm is successful and 94.2% of the objects were correctly identified as the corresponding pairs.

The Mixed Least Squares Meshfree (MDLSM) method has shown its appropriate efficiency for solving Partial Differential Equations (PDEs) governing the engineering problems. The method is based on the minimizing the residual functional. The residual functional is defined as a summation of the weighted residuals on the governing PDEs and the boundaries. The Moving Least Squares (MLS) is usually applied in the MDLSM method for constructing the shape functions. Although the required consistency and compatibility for the approximation function is satisfied by the MLS, the method loss its appropriate efficiency when the nodal points cluster too much. In the current study, the mentioned drawback is overcome using the novel approximation function called Mapped Moving Least Squares (MMLS). In this approach, the cluster of closed nodal points maps to standard nodal distribution. Then the approximation function and its derivatives compute noting the some consideration. The efficiency of suggested MMLS for overcoming the drawback of MLS is evaluated by approximating the mathematical function. The obtained results show the ability of suggested MMLS method to solve the drawback. The suggested approximation function is applied in MDLSM method, and used for solving the Burgers equations. Obtained results approve the efficiency of suggested method.