Multilevel thresholding is an important approach for image segmentation which has drawn much attention during the past few years. Traditional methods for multilevel thresholding are computationally expensive because they use the exhaustive searching strategy. To overcome the problem, metaheuristic algorithms are widely applied in this research area for searching the optimal thresholds recently. In this paper, a modified flower pollination algorithm (MFPA), as a novel improved metaheuristic algorithm, is proposed for multi-level thresholding. Two modifications are proposed to improve the original flower pollination algorithm (FPA). Firstly, a fitness Euclidean-distance ratio strategy is employed to modify the local-pollination of the original flower pollination algorithm (FPA). Secondly, the global-pollination in the original FPA is also biologically modified to improve exploration. Experiments are conducted between seven state-of-the-art metaheuristic algorithms and the proposed one. Both real-life images and remote sensing images are used in the experiments to test the performance of the involved algorithms. The experimental results significantly demonstrate the superiority of our method in terms of the objective function value, image quality measures and convergence performance.

Particle Swarm Optimization (PSO) is a well-known meta-heuristic. It has been widely used in both research and engineering fields. However, the original PSO generally suffers from premature convergence, especially in multimodal problems. In this paper, we propose a double-group PSO (DG-PSO) algorithm to improve the performance. DG-PSO uses a double-group based evolution framework. The individuals are divided into two groups: an advantaged group and a disadvantaged group. The advantaged group works according to the original PSO, while two new strategies are developed for the disadvantaged group. The proposed algorithm is firstly evaluated by comparing it with the other five popular PSO variants and two state-of-the-art meta-heuristics on various benchmark functions. The results demonstrate that DG-PSO shows a remarkable performance in terms of accuracy and stability. Then, we apply DG-PSO to multilevel thresholding for remote sensing image segmentation. The results show that the proposed algorithm outperforms five other popular algorithms in meta-heuristic-based multilevel thresholding, which verifies the effectiveness of the proposed algorithm.

Hyperbola detection is an important application field of ground-penetrating radar (GPR) systems, as underground threats and targets detected by these systems are presented in the form of hyperbola. However, the low-amplitude hyperbola detection of deeply-buried targets and targets with low metal content has remained a major challenge owing to the increase in the attenuation of radar echo with an increase in the detection depth and the features with low resolution extracted by existing methods. In this study, first, a high-level visual feature, namely, phase symmetry, is proposed to effectively improve the feature resolution and the robustness of amplitude change in GPR images. And then, we propose a handcrafted feature descriptor based on phase symmetry, namely, histogram of oriented vector phase symmetry (HOVPS) to improve the detection of hyperbola in GPR images. In constructing HOVPS, we first cite our previous work to enhance the descriptive ability of hyperbola in GPR images. Subsequently, we extend a phase symmetry model to develop a vector feature model (namely, vector phase symmetry, VPS). Lastly, HOVPS is developed based on the VPS to extract structural information from GPR images. The proposed HOVPS describes the shape features in GPR images using a symmetrical structure and it is used as the feature input of the classifier for hyperbola detection. Qualitative analysis of the proposed method is performed by comparing the performance of the proposed method for the extraction of features on different GPR data with those of other methods. In addition, we also provide quantitative analysis on different signal-to-noise ratio (SNR) test sets, and the results reveal that the proposed method outperforms various state-of-the-art methods (e.g., GPR histogram of oriented gradient, edge histogram descriptor, and log-Gabor feature).

Stable and reliable autonomous localization technology is fundamental for realizing autonomous driving. Localization systems based on global positioning system (GPS), cameras, LIDAR, etc., can be affected by building occlusion or drastic changes in the environment. These effects can degrade the localization accuracy and even cause the problem of localization failure. Localizing ground-penetrating radar (LGPR) as a new type of localization can rely only on robust subsurface information for autonomous localization. LGPR is mostly a 2D-2D registration process. This paper describes the LGPR as a slice-to-volume registration (SVR) problem and proposes an end-to-end TSVR-Net-based regression localization method. Firstly, the information of different dimensions in 3D data is used to ensure the high discriminative power of the data. Then the attention module is added to the design to make the network pay attention to important information and high discriminative regions while balancing the information weights of different dimensions. Eventually, it can directly regress to predict the current data location on the map. We designed several sets of experiments to verify the method’s effectiveness by a step-by-step analysis. The superiority of the proposed method over the current state-of-the-art LGPR method is also verified on five datasets. The experimental results show that both the deep learning method and the increase in dimensional information can improve the stability of the localization system. The proposed method exhibits excellent localization accuracy and better stability, providing a new concept to realize the stable and reliable real-time localization of ground-penetrating radar images.

Ground penetrating radar (GPR) is one of the most generally used underground sensing equipment, but it is frequently contaminated by clutter and noise during data acquisition, which has a significant impact on the detection performance of buried targets. The purpose of this letter is to present a novel clutter suppression method based on the principal component Gaussian curvature decomposition (PCGCD). First, the GPR B-scan data are divided into different sub-components using principal component analysis (PCA). Then, a Gaussian curvature decomposition (GCD) method is proposed, which can be applied to PCA domain subspaces to recover more target structure information from random noise. The PCGCD method’s performance is evaluated using both numerical simulation and real-world GPR datasets. The visualization and quantitative results demonstrated our method’s superiority in protecting the underground target structure, removing complex random noise, and improving the detection ability of buried targets.

A medical image registration method is proposed. An enhanced mutual information is employed as the similarity measure. A hybrid optimisation technique based on Powell's method and cuckoo search is developed as the optimiser. Experimental results show the effectiveness of the proposed method.