Sliding Windows Method Based on Terrain Self-Similarity for Higher DEM Resolution in Flood Simulating Modeling

A digital elevation model (DEM) is a quantitative representation of terrain and an important tool for Earth science and hydrological applications. A high-resolution DEM provides accurate basic Geodata and plays a crucial role in related scientific research and practical applications. However, in reality, high-resolution DEMs are often difficult to obtain. Due to the self-similarity present within terrains, we proposed a method using the original DEM itself as a sample to expand the DEM using sliding windows method (SWM) and generate a higher resolution DEM. The main processes of SWM include downsampling the original DEM and constructing mapping sets, searching for the optimal matching, window replacement. Then, we repeat these processes with the small-scale expansion factor. In this paper, the grid resolution of the Taitou Basin was expanded from 30 to 10 m. Overall, the superresolution reconstruction results showed that the method could achieve better outcomes than other commonly used techniques and exhibited a slight deviation (root mean square error (RMSE) = 3.38) from the realistic DEM. The generated high-resolution DEM prove to be significant in the application of flood simulation modeling.

Multiple Channel Electrocardiogram QRS Detection by Temporal Pattern Search

A highly constrained temporal pattern search based multiple channel heartbeat detector (TEPS) is described. TEPS generates sequences of peaks and statistically scores them according to: 1) peak time coherence across channels; 2) peak prominence; 3) temporal regularity; and 4) number of skipped beats. TEPS was tested on 31 records of three channel capacitive electrode data from the UnoViS automobile database. TEPS showed a sensitivity (SE) of 91.3% and a false discovery rate (FDR) of 3.0% compared to an SE and FDR of 75.3% and 65.0% respectively for a conventional single channel detector (OSEA) applied separately to the three channels. The peak matching window was 30ms. The percentage of 5 second segments with average heart rates within 5 beats/minute of reference was also measured. In 6 of the 31 records, the TEPS percentage was at least 30% greater than that of OSEA. TEPS was also applied to synthetic data comprising a known signal corrupted with calibrated amounts of noise. At a fixed SE of 85%, increasing the number of channels from one to two resulted in an improvement of approximately 5dB in noise resistance, while increasing the number of channels from two to four resulted in an improvement of approximately 3dB in noise resistance. The quantification of noise resistance as a function of the number of channels could help guide the development of wearable electrocardiogram monitors.

Stereo Dense Image Matching by Adaptive Fusion of Multiple-Window Matching Results

Traditional stereo dense image matching (DIM) methods normally predefine a fixed window to compute matching cost, while their performances are limited by the matching window sizes. A large matching window usually achieves robust matching results in weak-textured regions, while it may cause over-smoothness problems in disparity jumps and fine structures. A small window can recover sharp boundaries and fine structures, while it contains high matching uncertainties in weak-textured regions. To address the issue above, we respectively compute matching results with different matching window sizes and then proposes an adaptive fusion method of these matching results so that a better matching result can be generated. The core algorithm designs a Convolutional Neural Network (CNN) to predict the probabilities of large and small windows for each pixel and then refines these probabilities by imposing a global energy function. A compromised solution of the global energy function is utilized by breaking the optimization into sub-optimizations of each pixel in one-dimensional (1D) paths. Finally, the matching results of large and small windows are fused by taking the refined probabilities as weights for more accurate matching. We test our method on aerial image datasets, satellite image datasets, and Middlebury benchmark with different matching cost metrics. Experiments show that our proposed adaptive fusion of multiple-window matching results method has a good transferability across different datasets and outperforms the small windows, the median windows, the large windows, and some state-of-the-art matching window selection methods.

EFFECT OF IMAGE MATCHING WINDOW SIZE ON SATELLITE JITTER FREQUENCY DETECTION

Satellite attitude jitter is a common and complex phenomenon for high-resolution satellites and it is detectable by multi-temporal image matching. This paper analyses the effect of image matching window size on jitter frequency detection. First, two simulation images with a given short time lag and line scanning frequency affected by a modelled jitter are generated based on the principle of dynamic imaging model. Then, the relative image distortions are obtained by dense image matching with different matching window size and the frequency is estimated through spectrum analysis of the obtained image distortions. The experimental results demonstrated the feasibility and reliability of high frequency jitter detection based on dense image matching, and the results indicated that the maximum detectable frequency is almost not affected by the change of image matching window size, which provided useful demonstration of image-based satellite jitter detection capacity.

Feasibility Analysis of Magnetic Navigation for Vehicles

Magnetic navigation is a promising positioning technique for scenarios where a global navigation satellite system (GNSS) is unavailable, such as for underwater submarines and aircraft in space. For ground scenarios, it faces more challenges, since the magnetic distribution suffers interference from surrounding objects such as buildings, bridges, and vehicles. It is natural to think how feasible it is to apply magnetic matching positioning to vehicles. In this paper, a theoretic distribution model is proposed to analyze the magnetic field around objects such as buildings, bridges, and vehicles. According to the experiments, it is shown that the proposed model matches the experimental data well. In addition, a comprehensive indicator metric is defined in this paper to describe the feasibility of the magnetic matching method based on the statistical characteristics of magnetic maps. The best length of matching window, anti-noise performance, and pre-comparison of positioning accuracy in different regions can be easily derived using the proposed comprehensive indicator metric. Finally, the metric is verified through a drive test using different building densities.

A NOVEL SUB-PIXEL MATCHING ALGORITHM BASED ON PHASE CORRELATION USING PEAK CALCULATION

The matching accuracy of homonymy points of stereo images is a key point in the development of photogrammetry, which influences the geometrical accuracy of the image products. This paper presents a novel sub-pixel matching method phase correlation using peak calculation to improve the matching accuracy. The peak theoretic centre that means to sub-pixel deviation can be acquired by Peak Calculation (PC) according to inherent geometrical relationship, which is generated by inverse normalized cross-power spectrum, and the mismatching points are rejected by two strategies: window constraint, which is designed by matching window and geometric constraint, and correlation coefficient, which is effective for satellite images used for mismatching points removing. After above, a lot of high-precise homonymy points can be left. Lastly, three experiments are taken to verify the accuracy and efficiency of the presented method. Excellent results show that the presented method is better than traditional phase correlation matching methods based on surface fitting in these aspects of accuracy and efficiency, and the accuracy of the proposed phase correlation matching algorithm can reach 0.1 pixel with a higher calculation efficiency.

A NOVEL SUB-PIXEL MATCHING ALGORITHM BASED ON PHASE CORRELATION USING PEAK CALCULATION

The matching accuracy of homonymy points of stereo images is a key point in the development of photogrammetry, which influences the geometrical accuracy of the image products. This paper presents a novel sub-pixel matching method phase correlation using peak calculation to improve the matching accuracy. The peak theoretic centre that means to sub-pixel deviation can be acquired by Peak Calculation (PC) according to inherent geometrical relationship, which is generated by inverse normalized cross-power spectrum, and the mismatching points are rejected by two strategies: window constraint, which is designed by matching window and geometric constraint, and correlation coefficient, which is effective for satellite images used for mismatching points removing. After above, a lot of high-precise homonymy points can be left. Lastly, three experiments are taken to verify the accuracy and efficiency of the presented method. Excellent results show that the presented method is better than traditional phase correlation matching methods based on surface fitting in these aspects of accuracy and efficiency, and the accuracy of the proposed phase correlation matching algorithm can reach 0.1 pixel with a higher calculation efficiency.

Three-Dimensional Information Acquisition Based on Artificial Compound Eye Vision System

In this paper, the artificial compound eye vision system is used to develop an effective algorithm for acquiring 3D information of object. According to compound eye vision system constructed, the imaging principle and correlation between the object point and the imaging point is analyzed. Then the cross correlation matching algorithm is present to extract the depth and obtain the 3D information. The influence factors such as number of matched elemental images and matching window size are considered and analyzed. The experimental is implemented to verify the validity of the proposed method.