Search Results (63)

Multi-modal remote sensing image (MRSI) matching suffers from severe nonlinear radiometric distortions and geometric deformations, and conventional feature-based techniques are generally ineffective. This study proposes a novel and robust MRSI matching method using the side window filter (MSWF). First, a novel side window scale space is constructed based on the side window filter (SWF), which can preserve shared image contours and facilitate the extraction of feature points within this newly defined scale space. Second, noise thresholds in phase congruency (PC) computation are adaptively refined with the Weibull distribution; weighted phase features are then exploited to determine the principal orientation of each point, from which a maximum index map (MIM) descriptor is constructed. Third, coarse position, orientation, and scale information obtained through global matching are employed to estimate image-pair geometry, after which descriptors are recalculated for precise correspondence search. MSWF is benchmarked against eight state-of-the-art multi-modal methods—six hand-crafted (PSO-SIFT, LGHD, RIFT, RIFT2, HAPCG, COFSM) and two learning-based (CMM-Net, RedFeat) methods—on three public datasets. Experiments demonstrate that MSWF consistently achieves the highest number of correct matches (NCM) and the highest rate of correct matches (RCM) while delivering the lowest root mean square error (RMSE), confirming its superiority for challenging MRSI registration tasks. Full article

Climate scientists, systems theorists, and policymakers increasingly suggest that global sustainability challenges stem from dysfunctional worldviews and values that drive individual and collective behaviors, undermining both human flourishing and planetary health. Recognizing that paradigmatic shifts in values and worldviews can arise from transformative experiences, this study employed Integral Inquiry in a mixed-methods design to examine the nature of the relationship between such experiences and engagement with sustainability. A sample of 145 adults was recruited based on self-identification of having undergone a life-changing experience and demonstrated evidence of transformative growth and integration. In the qualitative phase, 73 participants completed an open-text survey detailing their perspectives on sustainability and their related practices and behaviors. Ten individuals from this subset were interviewed to explore the depth and dimensions of their engagement with sustainability. Using Constructivist Grounded Theory analysis, three tentative themes emerged: intraconnection, personal equilibrium, and defining social change. Whilst the study was exploratory in nature, the analysis indicated that transformative experiences seemed to foster a profound felt sense of intraconnection—a deep awareness of interconnectedness with all life. This awareness appeared to naturally clarify participants’ values and beliefs, aligning their actions toward sustainability. Moreover, participants emphasized the importance of cultivating personal equilibrium—a state of inner balance and congruence in daily choices—as a foundation for meaningful social and environmental change. This study tentatively highlights the role transformative experiences can play in bringing about more pro-ecological behavior, and it underscores the need for further research into how such experiences can be more readily integrated to support global sustainability efforts. Full article

Accurate image registration is essential for synthetic aperture radar (SAR) applications such as change detection, image fusion, and deformation monitoring. However, SAR image registration faces challenges including speckle noise, low-texture regions, and the geometric transformation caused by topographic relief due to side-looking radar imaging. To address these issues, this paper proposes a novel two-stage registration method, consisting of pre-registration and fine registration. In the pre-registration stage, the scale-invariant feature transform for the synthetic aperture radar (SAR-SIFT) algorithm is integrated into an iterative optimization framework to eliminate large-scale geometric discrepancies, ensuring a coarse but reliable initial alignment. In the fine registration stage, a novel similarity measure is introduced by combining frequency-domain phase congruency and spatial-domain gradient features, which enhances the robustness and accuracy of template matching, especially in edge-rich regions. For the topographic relief in the SAR images, an adaptive local stretching transformation strategy is proposed to correct the undulating areas. Experiments on five pairs of SAR images containing flat and undulating regions show that the proposed method achieves initial alignment errors below 10 pixels and final registration errors below 1 pixel. Compared with other methods, our approach obtains more correct matching pairs (up to 100+ per image pair), higher registration precision, and improved robustness under complex terrains. These results validate the accuracy and effectiveness of the proposed registration framework. Full article

The registration of aerial images and satellite images is a key step in leveraging complementary information from heterogeneous remote sensing images. Due to the significant intrinsic differences, such as scale, radiometric, and temporal differences, between the two types of images, existing multimodal registration methods tend to be either inaccurate or unstable when applied. This paper proposes a coarse-to-fine registration method for aerial images and satellite images based on the multiscale iteration method (MSIM). Firstly, an image pyramid is established, and feature points are extracted based on phase congruency. Secondly, the expression form of image descriptors is improved to more accurately describe image feature points, thereby increasing the matching success rate and achieving coarse registration between images. Finally, multiscale iterations are performed to find accurate matching points from top to bottom to achieve fine registration between images. In order to verify the effectiveness and accuracy of the algorithm, this paper also establishes a set of registration datasets of aerial and satellite captured images. Experimental results show that the proposed algorithm has high accuracy and good robustness, and effectively solves the problem of registration failure in existing algorithms when dealing with heterogeneous remote sensing images that have large scale differences. Full article

The application prospects of multi-temporal 3D models are broad. It is difficult to ensure that multi-temporal 3D models have a consistent spatial reference. In this study, a method for automatic alignment of multi-temporal 3D models based on phase congruency (PC) matching is proposed. Firstly, the texture image of the multi-temporal 3D model is obtained, and the key points are extracted from the texture image. Secondly, the affine model between the plane of the key point and its corresponding tile triangle is established, and the 2D coordinates of the key point are mapped to 3D spatial coordinates. Thirdly, multi-temporal 3D model matching is completed based on PC to obtain a large number of evenly distributed corresponding points. Finally, the parameters of the 3D transformation model are estimated based on the multi-temporal corresponding points, and the vertex update of the 3D model is completed. The first experiment demonstrates that the method proposed in this study performs remarkably well in improving the positioning accuracy of feature point coordinates, effectively reducing the mean error of the systematic error to below 0.001 m. The second experiment further reveals the significant impact of different 3D transformation models. The experimental results show that the coordinates obtained based on position and orientation system (POS) data have significant positioning errors, while the method proposed in this study can reduce the coordinate errors between the two-period models. Due to the fact that this method does not require obtaining ground control points (GCPs) and does not require manual measurement for 3D geometric registration, its application to multi-temporal 3D models can ensure high-precision spatial referencing for multi-temporal 3D models, streamlining processes to reduce resource intensity and enhancing economic efficiency. Full article

The automatic extraction of large-scale tie points (TPs) for Synthetic Aperture Radar (SAR) images is crucial for generating SAR Digital Orthophoto Maps (DOMs). This task involves matching SAR images under various conditions, such as different resolutions, incidence angles, and orbital directions, which is highly challenging. To address the feature extraction challenges of different SAR images, we propose a Gamma Modulated Phase Congruency (GMPC) model. This improved phase congruency model is defined by a Gamma Modulation Filter (GMF) and an adaptive noise model. Additionally, to reduce layover interference in SAR images, we introduce a GMPC-Harris feature point extraction method with layover perception. We also propose a matching method based on the SAR Modality Independent Neighborhood Fusion (SAR-MINF) descriptor, which fuses feature information from different neighborhoods. Furthermore, we present a graph-based overlap extraction algorithm and establish an automated workflow for large-scale TP extraction. Experiments show that the proposed SAR-MINF matching method increases the Correct Match Rate (CMR) by an average of 31.2% and the matching accuracy by an average of 57.8% compared with other prevalent SAR image matching algorithms. The proposed TP extraction algorithm can extract full-degree TPs with an accuracy of less than 0.5 pixels for more than 98% of 2-degree TPs and over 95% of multidegree TPs, meeting the requirements of DOM production. Full article

The automatic registration of infrared and visible images in vehicular imaging systems remains challenging in vision-assisted driving systems because of differences in imaging mechanisms. Existing registration methods often fail to accurately register infrared and visible images in vehicular imaging systems due to numerous spurious points during feature extraction, unstable feature descriptions, and low feature matching efficiency. To address these issues, a robust and efficient registration of infrared and visible images for vehicular imaging systems is proposed. In the feature extraction stage, we propose a structural similarity point extractor (SSPE) that extracts feature points using the structural similarity between weighted phase congruency (PC) maps and gradient magnitude (GM) maps. This approach effectively suppresses invalid feature points while ensuring the extraction of stable and reliable ones. In the feature description stage, we design a rotation-invariant feature descriptor (RIFD) that comprehensively describes the attributes of feature points, thereby enhancing their discriminative power. In the feature matching stage, we propose an effective coarse-to-fine matching strategy (EC2F) that improves the matching efficiency through nearest neighbor matching and threshold-based fast sample consensus (FSC), while improving registration accuracy through coordinate-based iterative optimization. Registration experiments on public datasets and a self-established dataset demonstrate the superior performance of our proposed method, and also confirm its effectiveness in real vehicular environments. Full article

An approach to detecting discontinuities in carbon fiber-reinforced polymers, caused by impact loading followed by compression testing, was developed. An X-ray sensor-based installation was used, while some algorithms were developed to improve the quality of the obtained low-contrast radiographic images with negligible signal-to-noise ratios. For epoxy/AF (#1) composite subjected to a “high-velocity” steel-ball impact with subsequent compression loading, it was not possible to detect discontinuities since the orientation of the extended zone of interlayer delamination was perpendicular to the irradiation axis. After drop-weight impacts with subsequent compression loading of epoxy/CF (#2) and PEEK/CF (#3) composites, the main cracks were formed in their central parts. This area was reliably detected through the improved radiographic images being more contrasted compared to that for composite #3, for which the damaged area was similar in shape but smaller. The phase variation and congruency methods were employed to highlight low-contrast objects in the radiographic images. The phase variation procedure showed higher efficiency in detecting small objects, while phase congruency is preferable for highlighting large objects. To assess the degree of image improvement, several metrics were implemented. In the analysis of the model images, the most indicative was the PSNR parameter (with a S-N ratio greater than the unit), confirming an increase in image contrast and a decrease in noise level. The NIQE and PIQE parameters enabled the correct assessment of image quality even with the S-N ratio being less than a unit. Full article

The innovative trend of “as a service” due to digital development and the rise of issues such as air pollution and traffic congestion led to the emergence of Mobility as a Service (MaaS) in the transportation sector. Companies and governments are experimenting to create a sustainable and efficient transportation future with MaaS. However, MaaS realization and business success from MaaS are still in their growing phase, making this study particularly relevant and timely. This study aims to identify the attributes of users’ selection of integrated mobility app services and the MaaS attributes that affect the behavioral intention to use through the mediation of perceived usefulness and perceived ease of use. This study marked four selection attributes—habit-congruence, information accuracy, relative advantage on efficiency, and IT system quality—for the integrated mobility app service, and 315 actual users of integrated mobility apps in Republic of Korea were sampled and analyzed. In terms of influence, information accuracy, relative advantage on efficiency, and habit-congruence significantly impacted perceived usefulness, in which habit-congruence had the most significant impact on perceived ease of use. In addition, habit-congruence and information accuracy were found to positively affect the behavioral intention to use, mediated by perceived usefulness and perceived ease. We also found that IT system quality was not a user selection attribute where this study was conducted. By providing empirical findings, this study can give management guidelines to companies and researchers in developing integrated mobility app service strategies to increase the number of users and maintain long-term customer relationships. Full article

Robust and efficient multi-source image matching remains a challenging task due to nonlinear radiometric differences between image features. This paper proposes a pixel-level matching framework for multi-source images to overcome this issue. Firstly, a novel descriptor called channel features of phase congruency (CFPC) is first derived at each control point to create a pixelwise feature representation. The proposed CFPC is not only simple to construct but is also highly efficient and somewhat insensitive to noise and intensity changes. Then, a Fast Sequential Similarity Detection Algorithm (F-SSDA) is proposed to further improve the matching efficiency. Comparative experiments are conducted by matching different types of multi-source images (e.g., Visible–SAR; LiDAR–Visible; visible–infrared). The experimental results demonstrate that the proposed method can achieve pixel-level matching accuracy with high computational efficiency. Full article

Deep eutectic solvents (DES) represent a promising class of green solvents, offering particular utility in the extraction and development of new formulations of natural compounds such as ferulic acid (FA). The experimental phase of the study undertook a systematic investigation of the solubility of FA in DES, comprising choline chloride or betaine as hydrogen bond acceptors and six different polyols as hydrogen bond donors. The results demonstrated that solvents based on choline chloride were more effective than those based on betaine. The optimal ratio of hydrogen bond acceptors to donors was found to be 1:2 molar. The addition of water to the DES resulted in a notable enhancement in the solubility of FA. Among the polyols tested, triethylene glycol was the most effective. Hence, DES composed of choline chloride and triethylene glycol (TEG) (1:2) with added water in a 0.3 molar ration is suggested as an efficient alternative to traditional organic solvents like DMSO. In the second part of this report, the affinities of FA in saturated solutions were computed for solute-solute and all solute-solvent pairs. It was found that self-association of FA leads to a cyclic structure of the $C_2^8$ type, common among carboxylic acids, which is the strongest type of FA affinity. On the other hand, among all hetero-molecular bi-complexes, the most stable is the FA-TEG pair, which is an interesting congruency with the high solubility of FA in TEG containing liquids. Finally, this work combined COSMO-RS modeling with machine learning for the development of a model predicting ferulic acid solubility in a wide range of solvents, including not only DES but also classical neat and binary mixtures. A machine learning protocol developed a highly accurate model for predicting FA solubility, significantly outperforming the COSMO-RS approach. Based on the obtained results, it is recommended to use the support vector regressor (SVR) for screening new dissolution media as it is not only accurate but also has sound generalization to new systems. Full article

Potato production systems present various phytosanitary problems. Among these, potato early dying (PED) caused by Verticillium spp. is a disease that is difficult to detect in its early stages and whose expression occurs in critical growing phases of the crop, such as tuber filling, generating a high economic impact. The objective of this work was to use spectral data to classify potato plants and identify the degree of severity of PED using spectral signatures and multispectral images captured on potato plants under greenhouse and commercial production conditions. Methods such as principal component analysis (PCA), random forest (RF), support vector machine (SVM), and artificial neural network (ANN) algorithms were implemented. All algorithms performed well; however, the RF was more accurate after iteration. The RF had a good capacity for indirect detection of PED, with an average accuracy of 60.9%. The wavelengths related to the red and red edges, especially from 710 to 735 nm, proved to be highly informative. As a result of the congruence between field and greenhouse data, the RECI, NDRE, VWI, and GRVI spectral indices were consistent with the discrimination of symptoms and PED severity levels. Identified wavelengths can be applied in the design of optical sensors that, together with the use of ML algorithms, can be implemented in the remote detection of early death in potato crops. Full article

The concept of Image Phase Congruency (IPC) is deeply rooted in the way the human visual system interprets and processes spatial frequency information. It plays an important role in visual perception, influencing our capacity to identify objects, recognize textures, and decipher spatial relationships in our environments. IPC is robust to changes in lighting, contrast, and other variables that might modify the amplitude of light waves yet leave their relative phase unchanged. This characteristic is vital for perceptual tasks as it ensures the consistent detection of features regardless of fluctuations in illumination or other environmental factors. It can also impact cognitive and emotional responses; cohesive phase information across elements fosters a perception of unity or harmony, while inconsistencies can engender a sense of discord or tension. In this survey, we begin by examining the evidence from biological vision studies suggesting that IPC is employed by the human perceptual system. We proceed to outline the typical mathematical representation and different computational approaches to IPC. We then summarize the extensive applications of IPC in computer vision, including denoise, image quality assessment, feature detection and description, image segmentation, image registration, image fusion, and object detection, among other uses, and illustrate its advantages with a number of examples. Finally, we discuss the current challenges associated with the practical applications of IPC and potential avenues for enhancement. Full article

The correct management of operations in thermoelectric plants is based on the continuous evaluation of the structural integrity of its components, among which there are elements made of stainless steel that perform water conduction functions at elevated temperatures. The working conditions generate progressive wear that must be monitored from the perspective of the microstructure of the material. When AISI 304 stainless steel is subjected to a temperature range between 450 and 850 °C, it is susceptible to intergranular corrosion. This phenomenon, known as sensitization, causes the material to lose strength and generates different patterns in its microstructure. This research analyzes three different patterns present in the microstructure of stainless steel, which manifest themselves through the following characteristics: the absence of intergranular corrosion, the presence of intergranular corrosion, and the precipitation of chromium carbides. This article shows the development of a methodology capable of recognizing the corrosion patterns generated in stainless steel with an accuracy of 98%, through the integration of a multilayer perceptron neural network and the following digital image processing methods: phase congruence and a gray-level co-occurrence matrix. In this way, an automatic procedure for the analysis of the intergranular corrosion present in AISI 304 stainless steel using artificial intelligence is proposed. Full article

Aiming at the nonlinear radiometric differences between multi-source sensor images and coherent spot noise and other factors that lead to alignment difficulties, the registration method of gradient weakly sensitive multi-source sensor images is proposed, which does not need to extract the image gradient in the whole process and has rotational invariance. In the feature point detection stage, the maximum moment map is obtained by using the phase consistency transform to replace the gradient edge map for chunked Harris feature point detection, thus increasing the number of repeated feature points in the heterogeneous image. To have rotational invariance of the subsequent descriptors, a method to determine the main phase angle is proposed. The phase angle of the region near the feature point is counted, and the parabolic interpolation method is used to estimate the more accurate main phase angle under the determined interval. In the feature description stage, the Log-Gabor convolution sequence is used to construct the index map with the maximum phase amplitude, the heterogeneous image is converted to an isomorphic image, and the isomorphic image of the region around the feature point is rotated by using the main phase angle, which is in turn used to construct the feature vector with the feature point as the center by the quadratic interpolation method. In the feature matching stage, feature matching is performed by using the sum of squares of Euclidean distances as a similarity metric. Finally, after qualitative and quantitative experiments of six groups of five pairs of different multi-source sensor image alignment correct matching rates, root mean square errors, and the number of correctly matched points statistics, this algorithm is verified to have the advantage of robust accuracy compared with the current algorithms. Full article