فهرست مطالب alireza safdari nezhad

Photometry is a well-known method for 3D reconstruction of objects using images taken in different lighting conditions. In this method, by knowing the light sources' direction, the normal vectors of the surface are recovered in a dense grid through the intensities recorded in the captured images. Each normal vector is then converted to the height difference in two orthogonal directions, and the simultaneous estimation of the heights for the dense grid is done by solving a system of linear, overdetermined and inconsistent equations. The miss-alignment of the coordinate system represents normal vectors and the dense grid frame of 3D reconstruction causes a systematic error in the estimation of the gridded heights map. Photometric self-calibration methods for determining the light sources’ direction are one of the causes of miss-alignments in object and surface normal vectors coordinate systems. In this paper, a sequential and iterative process is proposed to estimate and perform an appropriate rotation to the surface normal vectors. In each iteration of this method, a portion of the necessary rotation is identified in order to parallelize of the two object coordinate systems and surface normal vectors through fitting a geometric transformation to the estimated residuals of the 3D reconstruction process. The results of using the proposed method in various experiments have demonstrated a noticeable improvement in the precision and accuracy of 3D reconstruction.

One way to ensure food security is to produce strategic agricultural products on a large scale using industrial methods. Managing large-scale farms consistently and cohesively is a challenging task that requires the utilization of modern technologies. Crop anomalies refer to uncommon and limited factors during agricultural production, leading to localized differentiation in the crop cultivation process. Factors contributing to crop anomalies in agriculture include imbalances in soil nutrients and fertilizers, grazing during crop growth, pests, variations in soil texture and slope in pastures, weed growth, and drought. Detecting and remediating factors limiting crop growth in vast agricultural lands is difficult and these issues are often noticed at harvest time. This article suggests a solution for continuously monitoring of large agricultural fields by analyzing the time series of Sentinel-2 satellite images. The effectiveness of this solution in detecting various anomalies of farms, in agrarian areas has been demonstrated by the results. The proposed solution offers features such as timely diagnosis, the ability to monitor the continuation of irregularities, and the measurement of compensatory measures' effectiveness. The method has successfully identified over five types of anomalies in the selected farms, achieving a detection accuracy of 95.60%.

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.

Map of croplands is one of the information layers required in the efficient management of these lands. Having such maps makes it possible to monitor agricultural fields during the growing season continuously. In this study, a solution to produce map of Shahrekord’s agricultural lands in two agricultural and non-agricultural classes is presented using the time series of different extracted indices from Sentinel-2 images. Since the use of large data sources is one of the obstacles to the development of methods based on the time series of satellite images, the Google Earth engine processing platform has been used in this study. The proposed method is based on integrating supervised pixel-based classification results with segmentation results. First, training data of supervised classification is provided in a rigorous refining process without the need of collected data from field surveys or interpretation of high-resolution satellite images. Then, by calculating the separability of the two target classes in the time series of each index, the optimal indices are selected. Finally, by combining the results of segmentation and classification methods based on the votes obtained from the classification results, agricultural or non-agricultural class is assigned to each of the image segments. In addition to incorporating spatial information including edges and spatial proximity, this method has been able to improve the noise and porous results of pixel-based classification and has increased the overall accuracy of the final map from 90.7% to 96.05%. Also, user accuracy of both agricultural and non-agricultural classes show an improvement of 3.27 and 7.97%, respectively.

In this paper, a novel method has been proposed to automatically produce 3D videos through amateur digital cameras that have been fixed with each other. Asynchrony of the videos (frames per second rates and start times), unavailability of the exact camera internal parameters, and the technical limitation of precise relative adjusting the stereo cameras could be known as the main challenges of producing 3D videos via the amateur cameras. In the proposed method, the videos acquired by the stereo camera are synchronized through the automatic matching between temporal indices. Then, the calm periods of videos (the times with zero relative velocity between camera and scenes) are detected to be used for selecting proper matched image frames. Proper matched frames are then applied for finding matched points via a geometrical constrained feature-based matching method. The matched points are used for self-calibration as well as relative parameters estimation of the stereo cameras. In the last step, epipolar resampling procedure has been used to generate normalized videos. Automatic and precise synchronization of the stereo videos as well as the proper generalization of the proposed approach in the different sample datasets has been seen in the evaluation processes. Spectator satisfaction in the depth perception of the 3D videos is another quality achievement of the proposed method.

Rational Function Models (RFMs) are known as the most famous mathematical transformations used in geometric correction of satellite images. Considering the lack of enough and well-distributed Ground Control Points (GCPs), the structure optimization is a critical step in the terrain-dependent RFM estimation strategy. Heretofore, the binary encoding Genetic Algorithm (GA) optimization method has been used to find the optimal structure of RFMs. However, randomized generation of initial population can directly impact the convergence and also computational costs. In this paper, an approach has been proposed to modify the initial population of the GA algorithm based on the correlations of the column vectors of the least square design matrix. In this approach, probability of the presence of each RFM term in the GA initial population is linearly dependent on its correlation with other terms. Although this method slightly decreases the geometric accuracy, it can fall the processing time by 37.02% on average.

Image fusion is known as a synergetic process for merging multispectral and panchromatic images contents. So far, various methods have been developed in which the usage of the frequency domain is one of them. The frequency-based image fusion techniques are performed using high and low pass filters. So, the determination of the sizes of these filters would be a challenge. In this paper, a weighted index is proposed to determine the sizes and shapes of the low and high filters in fusion of the panchromatic and multispectral images. In the proposed method, the weights of the spectral and spatial indicators are independently estimated for each image. So, the effects of the differentiation of the image contents and different range of the indicators are properly adjusted to reach the optimum filtering. The comparison of the best results obtained from the proposed method with the other well-known fusion methods, in the used datasets, was indicated an average improvement of 58% in RMSEs.

Rational Function Models (RFMs) are known as the most famous mathematical transformations used in geometric correction of satellite images. Considering the lack of enough and well-distributed Ground Control Points (GCPs), the structure optimization is a critical step in the terrain-dependent RFM estimation strategy. Heretofore, the binary encoding Genetic Algorithm (GA) optimization method has been used to find the optimal structure of RFMs. However, randomized generation of initial population can directly impact the convergence and also computational costs. In this paper, an approach has been proposed to modify the initial population of the GA algorithm based on the correlations of the column vectors of the least square design matrix. In this approach, probability of the presence of each RFM term in the GA initial population is linearly dependent on its correlation with other terms. Although this method slightly decreases the geometric accuracy, it can fall the processing time by 37.02% on average.

In this paper, a simple structured light system is designed to produce the three-dimensional points cloud from the un-textured surfaces. The system consists of two cameras and a planer laser, in which the 3D contents are produced through the stereo images taken from the light reflected by the intersection of a planer laser and the 3D surface of an object. There was no control over how the laser swept through the surface and the instantaneous parameters of the laser plane were not known in advance. Considering the knowledge of the internal camera calibration parameters and the relative orientation of the stereo-pairs, the video captured by the cameras are normalized during the epipolar re-sampling process. Next, in each pair of simultaneous frames, the matched points located at the 3D section of the laser’s plane are then identified. During the simultaneous space intersection of the matched points, a constraint is applied to enforce the singularity of the covariance matrix of 3D points lie in the intersection of the laser's plane and the 3D surface of an object to ensure their co-planarity. By applying this statistical constraint, the precision of the surface 3D reconstruction was improved up to 41% in this structured light system.

High accuracy and huge density of 3D points cloud acquired by airborne Lidar makes them as a good and suitable tool in order to analyze of terrain surface. In this procedure, points cloud clustering is a fundamental step in the procedure of information extraction form LiDAR's data. In this paper a novel method is proposed for supervised classification of LiDAR points cloud based on contextual analysis on LiDAR points. The proposed method consists of three main steps. In the first step, a set of contextual features are produced for each points in LiDAR data. In second step, optimum feature selection is done in the modified prototype space using a new strategy. The last step is conducted to a simple k-means clustering on the feature space spanned by optimum contextual clusters. An urban area with the residential texture has been used as the case study to evaluation of the proposed method. The results indicate proper classification accuracies. The overall accuracies and kappa coefficients was 93.15% and 0.89 respectively.