A Fast Mosaic Approach for Remote Sensing Images

An approach of fast image mosaic is presented, which involves image matching and image intensity smoothing. Image matching includes two procedures, i.e. rough matching and fine matching. In rough matching, the overlapped regions of two adjacent images to be mosaicked are segmented to binary image at first. Then the binary images are filtered by open operation of mathematic morphologic method. In the binary image region of the reference image, feature template is searched and extracted on a given rule. Via XOR operation of the feature template and search region, some possible matching positions in the overlapped region of the other image are got. In the fine matching, the sequential similarity detection algorithm (SSDA) is adopted to perform matching computation in the small regions near the positions got in the rough matching, and then the relative position offsets in X-orientation and Y-orientation between the two adjacent images are got. Based on the result of the image matching, the two images are stitched. An approach of seam-line smoothing is adopted to adjust the intensity of the overlapped area. Simulation experimental results show that the approach greatly improves the operation speed, while the precision remains fine, so it can be applied in real-time mosaicking.

Based on the values of external orientation elements of the camera installed on the unmanned aerial vehicle (UAV), the nearest-neighbor interpolation is adopted to rectify original images geometrically. The range of the overlapping region between two consecutive images is determined by the flying status parameters of UAV. Both of the overlapping regions of the two images are processed to binary image at first. In the appointed binary image region of the former image, feature template is searched and extracted. By the XOR operation, some possible matching positions of the latter image are got. Furthermore, normalized correlation method (NC) is adopted to validate the optimal matching position, by which two consecutive images are mosaicked to an image. An intensity transformation function between the two consecutive images is built to smooth the mosaicked image. Simulation experimental results show that the approach presented is effective.

In a binary image, pixels can take on exactly one of two values. These values are often thought of as representing the “foreground” and “background” in the image, even though these concepts often are not applicable to natural scenes. In this chapter we focus on connected regions in images and how to isolate and describe such structures.

Based on analyzing the traditional image matching algorithms, a fast image matching method, which integrates improved sequential similarity detection algorithm (SSDA) and wavelet transformation, is presented in this paper. Firstly, some basic principles of SSDA image matching and wavelet transformation are introduced. Secondly, according to the shortcoming of traditional SSDA algorithm, a new SSDA algorithm is studied for improving the determining velocity of the threshold in SSDA searching match. By integrating wavelet transformation, an integrated matching algorithm is given out. Finally, the new method is compared with the traditional matching algorithms in experiments. The experimental results show that the new method has higher efficiency and accuracy in image matching and is suitable for the realization of real-time underwater navigation.

In allusion to the colored image matching characteristic in the system of robot view navigation, SSDA (the sequential similarity detection algorithm) is improved and adaptive genetic algorithm is brought in; meanwhile, level-divided search strategy connective with rough and exact matching. The improved algorithm can enhance the image matching speed with no matching accuracy reduced, so that real-time requirements of robot view navigation can be met and robot view navigation will be of preferable robustness.

This paper proposes an efficient method for writer identification in handwritten documents. The method operates at two stages: rough matching and fine matching. At the first stage, the histograms of the first and second order differential chain codes are extracted and used in rough matching. At the second stage, the histograms of the positions of connected edge pixel pairs in a floating grid are recorded and applied to the fine matching. At each stage, two writings are compared by computing the distances between their respective histograms. The experiments are implemented on two common data sets: HIT-MW and IAM, which contain samples of Chinese and English handwritten texts. The experimental results show that the proposed methods achieve promising results on writer identification.KeywordsWriter identificationGlobal levelLocal levelRough matchingFine matching

Optical flow (OF) plays a decisive role in visual situation awareness, detection and obstacle avoidance systems for unmanned aerial vehicles (UAVs), which are cyberphysical systems (CPSs) that interact with the environment through sensors and actuators. The use of cameras allows the integration of computer vision (CV) algorithms with the inertial navigation systems (INS). The movement of characteristics of the image fused with the dynamic of the UAVs allows us to improve the process of remoting sense, avoid obstacles or estimate the position and velocity of the UAV. In the literature, there are various algorithms to locate characteristics points between two consecutive images. However, the computation time and consumption of physical resources such as memory features are due to embedded systems. This chapter shows (i) how to integrate the movement of the pixel textures (OF) in the image with INS data, (ii) compares different algorithms to match points between consecutive images, (iii) implements a process to encounter points between consecutive images and (iv) implements a computationally less expensive and with less memory consumption algorithm. A case study about using the field-programmable gate array (FPGA) as part of the visual servoing is discussed showing how to integrate results into the CV hardware system of a UAV and addressing the need to handle issues such as multi-resolution.

Aim. Determination of the elements of external spatial orientation of the surveying systems at the moment of image acquisition is the fundamental task in photogrammetry. Principally, this problem is solving in two ways. The first way is direct positioning and measuring of directions of camera optical axis in the geodetic space with the help of GNSS/INS equipment. The second way is the analytical solution of the problem using a set of reference information (often such information is a set of ground control points whose geodetic positions are known with sufficient accuracy and which are reliably recognised on aerial images of the photogrammetric block). The authors consider the task of providing reference and control information using the second approach, which has a number of advantages in terms of reliability and accuracy of determining the unknown image exterior orientation parameters. It is proposed to obtain additional images of ground control points by the method of their auxiliary aerial photography using an unmanned aerial vehicle (UAV) on a larger scale compared to the scale of the images of the photogrammetric block. The aim of the presented work is the implementation of the method of creating reference points and experimental confirmation of its effectiveness for photogrammetric processing. Methods and results. For the entire realization of the potential of the analytical way to determine the elements of external orientation of images, it is necessary to have a certain number of ground control points (GCP) and to keep the defined scheme of their location on the photogrammetric block. As the main source of input data authors use UAV aerial images of the terrain, which are obtained separately from the block of aerial survey, and have a better geometric resolution and which clearly depict the control reference points. Application of such auxiliary images gives the possibility of automated transferring of the position of ground control point into images of the main photogrammetric block. In our interpretation, these images of ground control points and their surroundings on the ground are called "control reference images". The basis of the work is to develop a method for obtaining the auxiliary control reference images and transferring of position of GCP depicted on them into aerial or space images of terrain by means of computer stereo matching. To achieve this goal, we have developed a processing method for the creation of control reference images of aerial image or a series of auxiliary multi-scale aerial images obtained by a drone from different heights above the reference point. The operator identifies and measures the GCP once on the auxiliary aerial image of the highest resolution. Then there is an automatic stereo matching of the control reference image in the whole series of auxiliary images in succession with a decrease in the resolution and, ultimately, directly with the aerial images of photogrammetric block. On this stage there are no recognition/cursor targeting by the human operator, and therefore there are no discrepancies, errors or mistakes related to it. In addition, if to apply fairly large size of control reference images, the proposed method can be used on a low-texture terrain, and therefore deal in many cases without the physical marking of points measured by GNSS method. And this is a way to simplify and reduce the cost of photogrammetric technology. The action of the developed method has been verified experimentally to provide the control reference information of the block of archival aerial images of the low-texture terrain. The results of the experimental approbation of the proposed method give grounds to assert that the method makes it possible to perform geodetic reference of photogrammetric projects more efficiently due to the refusal of the physical marking of the area before aerial survey. The proposed method can also be used to obtain the information for checking the quality of photogrammetric survey for provision of check points. The authors argue that the use of additional equipment - UAV of semi-professional class to obtain control reference images is economically feasible. Scientific novelty and practical relevance. The results of approbation of the "control reference image" method with obtaining stereo pairs of aerial images with vertical placement of the base are presented for the first time. There was implemented the study of the properties of such stereo pairs of aerial images to obtain images of reference points. The effectiveness of including reference images in the main block of the digital aerial triangulation network created on UAV’s images is shown.

Template matching is a basic algorithm for image processing, and real-time is a crucial requirement of object tracking. For real-time tracking, a fast template matching algorithm based on grey prediction is presented, where computation cost can be reduced dramatically by minimizing search range. First, location of the tracked object in the current image is estimated by Grey Model (GM). GM(1,1), which is the basic model of grey prediction, can use some known information to foretell the location. Second, the precise position of the object in the frame is computed by template matching. Herein, Sequential Similarity Detection Algorithm (SSDA) with a self-adaptive threshold is employed to obtain the matching position in the neighborhood of the predicted location. The role of threshold in SSDA is important, as a proper threshold can make template matching fast and accurate. Moreover, a practical weighted strategy is utilized to handle scale and rotation changes of the object, as well as illumination changes. The experimental results show the superior performance of the proposed algorithm over the conventional full-search method, especially in terms of executive time.

Losses caused by various disasters are continuing to rise as the global disasters frequently occur in recent years. In order to reduce the losses, UAV (unmanned aerial vehicle) technologies are increasingly employed in the emergency management field. Rapid emergency response has a strong demand for fast UAV image processing, while image mosaicking is one of the most important requirements. Whether an UAV route is appropriately designed has significant effects on the quality of UAV collected images, the mosaicking efficiency, and stability. In this paper, fast image mosaicking technologies for emergency management are introduced and its practical problems during image processing are analyzed. Based on that, an UAV route planning mathematical model is proposed which is used for fast image mosaicking. The given model considers three different approaches covering most of UAV flight task situations.

By combining wavelet transform and increment sign correlation with iterative correction, we present a robust image matching method, which effectively reduced mismatch. Firstly, by decomposing the sensed image and the reference image with wavelet, smooth images are generated in coarse scales. By the use of increment and decrement information of adjacent pixels, smooth images are encoded into binary images, and the matching position can be found by use of the cross correlation function. Wavelet decomposition can effectively remove noise and greatly improve the real-time performance of matching algorithm while increment sign correlation method is not sensitive to the gray level of images. The combination of wavelet transform and increment sign correlation can deal with noise, shadow and occlusion as well as improve the matching speed compared with traditional matching methods. Considering the condition of heavy noise and occlusion that the matching method mentioned above can not find the correct position in condition of heavy noise and occlusion, we present an iterative correction image matching method, i.e. use the above matching result as prior knowledge and achieve the correct matching position by correcting matching results iteratively. Theoretical analysis shows the feasibility of the presented method, and our experiments show its validity.

The object tracking algorithm based on siamese network converts the object tracking problem into the problem of the similarity matching between the feature maps of the template branch and the search branch. Currently, most trackers use the entire template feature as a kernel to match the similarity of features with the search region, while this global matching approach does not cope well with rapid changes in the target. To solve this problem, we construct a graph for template and search features and embed the template feature information into the search features through multiple graph attentions. The proposed method is evaluated on advanced test benchmarks such as OTB100 and GOT10k and compared with some of the latest algorithms. Experimental results demonstrate the excellent performance of our algorithm, with significant advantages on multiple datasets.

Precise navigation is crucial for the long range unmanned aerial vehicles (UAVs). But today’s UAVs still rely on global navigation satellite system (GNSS) for accurate navigation; this brings in the safety issues as the weak satellite signals can be interfered. To achieve accurate autonomous navigation, we propose to utilize publicly available remote sensing maps to compensate the drifts from the inertial navigation systems. To deal with severe visual variations between the camera images and the map image, we propose a hierarchical outlier filtering strategy to filter out the feature correspondence with a voting algorithm at the first stage, filter out the wrong matched position with the predictions from the inertial navigation system at the second stage. Then an Extended Kalman Filter (EKF) is constructed to combine the matched position with the Inertial Navigation System (INS) to achieve accurate and drift free localization performance. To test the proposed algorithm, we have conducted long range real flight experiments with a CH-4 UAV and the overall localization error is less than 10 m.KeywordsImage matching locationIntegrated navigationLong range UAV

The high-precision navigation and positioning ability of UAV (Unmanned Aerial Vehicles) is the key factor to reflect its degree of automation. Visual navigation based on image matching has become one of the important research fields for UAV to realize autonomous navigation, because of its low cost, strong anti-jamming ability, and good location result. However, the visual quality of images captured by UAV will be seriously affected by some factors, such as weak illumination conditions or insufficient performance of its sensors. Resolving a series of degradation of low-light images can improve the visual quality and enhance the performance of UAV visual navigation. In this paper, we propose a novel fully convolutional network based on the Retinex theory to solve the degradations of low-light images captured by UAV, which can improve the visual quality of the images and visual matching performance effectively. At the same time, a visual navigation system is designed based on the proposed network. Extensive experiments demonstrate that our method outperforms the existing methods by a large margin both quantitatively and qualitatively, and effectively improves the performance of the image matching algorithms. The visual navigation system can successfully realize the self-localization of UAV under different illumination conditions. Moreover, we also prove that our method is also effective in other practical tasks (e.g. autonomous driving).

Hierarchical motion consistency constraint for efficient geometrical verification in UAV stereo image matching