Gray Information Research Articles

Enhancing robustness of community structure in networks against attacks with gray information

In recent years, network science has received significant attention and application. As an important mesoscale structure of networks, community structure reveals the inherent modular structure and potential functionality of networks. Therefore, maintaining the community structure during attacks is crucial. Existing studies on community robustness primarily focus on two attack behaviors: malicious attacks and random failures. However, little is known on the community robustness in the more practical situations where the attackers have limited ability to access precise network information, leading to gray information attack behaviors. Hence, in this paper, we investigate the robustness of network communities under gray information attacks and the enhancing algorithms. Following the community robustness measure for the sequential attacks, we establish a unified evaluation framework for attacks with gray information by introducing a gray attack coefficient, which treats the usual random failures and malicious attacks as the two special situations in the proposed framework. Several enhancing algorithms with local search strategy are exquisitely devised. The efficacy of our framework and algorithms which significantly improve the community robustness against gray attacks is demonstrated by the experimental results on a variety of real-world networks. Our findings have important implications not only in enhancing the community robustness of existing networks but also in designing robust ones from the scratch, which paves the way to further understand and seek strategies and solutions for network community robustness with gray information.

Unveiling sequential layered heterogeneity in multi-spectral transmission images clustering through iterative terrace compression with adaptive neighborhoods analysis

Multi-spectral transmission imaging looks forward to an early breast cancer screening with cost-effectiveness, safety, and ease of use. However, the biological tissue of the breast owns scattering and strong absorption properties, resulting in transmission images with a low signal-to-noise ratio and prior investigation focused on detection heterogeneity within a single layer, but the different depths in heterogeneity due to the spherical shape of the breast causing the challenges. Accurately detecting heterogeneity in different layers is crucial due to low contrast, veiling layers, and unclear boundaries, which can be difficult to identify in clustering and segmentation. The breast heterogeneity identification can be handled by combining image processing techniques; nevertheless, considering labeling limits and data accessibility, the clustering algorithm provides an acceptable substitute. Therefore, the paper proposes a novel methodology integrating the iterative terrace compression method with adaptive neighborhoods analysis to identify sequential multi-layered heterogeneity and enhance the clustering of multi-spectral transmission images. Our experimental setup involved the collection of low grayscale images across six wavelengths, followed by advanced image processing techniques, including frame accumulation to improve image signal-to-noise ratio and then polynomial surface fitting to eliminate trend items from uneven light. After that, iterative terrace compression with adaptive neighborhoods analysis is applied to reduce redundancy and unveil heterogeneity in different layers and window functions to remove unnecessary gray information. Finally, clustering combined with pseudo-color is used for clustering analysis and visual representation for aiding in heterogeneity detection. Results indicate a significant improvement in detection metrics, with our method outpacing existing techniques regarding the Dice coefficient, F1 score, and Jaccard. This innovative approach not only enhances the characterization of breast tissue heterogeneities in different layers but also promises practical clinical applications in breast cancer screening.

A novel multi-attribute three-way decision model with three-parameter interval grey number decision-theoretic rough sets

PurposeTo overcome the limitations of traditional multi-attribute decision making (MADM) methods, which only provide deterministic rankings of decision objects, this paper proposes a novel multi-attribute 3WD model. This model presents three-parameter interval grey number decision-theoretic rough sets (TPIGNDTRSs), aiming to offer a reasoned interpretation of loss functions in grey environments and ensure objective assessment of conditional probabilities.Design/methodology/approachFirstly, the traditional equivalence relation is replaced with the probabilistic dominance relation (PDR), categorizing decision objects into two state sets in DTRS for more objective conditional probabilities. Secondly, as the three-parameter interval grey number (TPIGN) introduces the most probable value on the basis of the traditional two-parameter interval grey number, it provides a more comprehensive method for describing grey information. Consequently, integrating TPIGN into DTRS refines the interpretations of loss functions in grey environments. Finally, by utilizing two main sorting techniques, relative kernel and degree of accuracy ranking and possibility ranking, two types of 3WD rules with TPIGNDTRSs, are constructed.FindingsThis study has successfully developed and validated a new multi-attribute 3WD model. The model was tested in two distinct domains: evaluating innovation efficiency in high-tech enterprises and recommending movies in a practical case. The findings reveal that the model can effectively integrate relevant information of high-tech enterprises, provide the government with enterprise-level assessments, and gather consumer preferences to recommend the most suitable movies.Research limitations/implicationsThis study treats the loss function as grey information in the 3WD model but overlooks the grey nature of evaluation values, limiting its applicability. Additionally, the model’s reliance on subjective expert judgments and historical data to establish the loss function may affect its objectivity. The implications of this research are that the novel model overcomes traditional MADM limitations, enhancing decision-making quality and efficiency in complex and grey scenarios. The model’s successful application in evaluating high-tech enterprises and recommending movies illustrates its dual value in both theory and practice.Originality/valueInitially, the model proposed in this study is of significant importance for the development of the 3WD field, as it successfully addresses the challenges of uncertain loss functions and unknown conditional probabilities in grey information environments. Moreover, by integrating the 3WD model with MADM problems, it has broken through the bottlenecks of traditional MADM methods, offering new perspectives and strategies for solving MADM issues. Therefore, this research not only advances theoretical research but also provides powerful tools for practical applications.

Small-sample linear profile error uncertainty assessment based on grey system

The uncertainty assessment of the profile error of the cam profile, as defined in the national standard method, is difficult to carry out under conditions of small sample size and absence of probability distribution assumptions. This paper proposes a small-sample assessment model for the uncertainty of the profile error based on grey system. Firstly, the coordinate transformation is conducted using Vector Alignment Method to reduce systematic errors, and the non-uniform rational B-splines curve interpolation is utilized to fit the cam profile curve and perform error assessment. Subsequently, based on the error assessment results, Grey Information Measurement Model (GIMM) for the uncertainty of the profile error in small samples is established. This model employs Grey Relational Analysis to eliminate outliers and evaluates the uncertainty of the profile error by solving grey correlation coefficients. Maximum-Minimum Information Measure Method is used to assess the optimal sample size. Finally, numerical experiments and experimental tests were conducted on the uncertainty of camshaft profile error in automobiles. A total of 15 sets of profile data were compared with Guide to the Representation of Uncertainty in Measurement (GUM) and Monte Carlo Method (MCM) under different sample sizes. The results showed that GIMM achieved evaluation with only 8 sets of data samples under small sample and poor information conditions, with an uncertainty of 0.6338 μm, compared to 0.6346 μm for GUM and 0.6391 μm for MCM. The acceptance rate of GIMM reached 95.2%. This model outperforms other methods, providing a simplified and reliable assessment of cam profile error uncertainty.

A new framework for project time–cost–environmental trade-off problem with hybrid Fermatean fuzzy–grey information

A new framework for project time–cost–environmental trade-off problem with hybrid Fermatean fuzzy–grey information

Hyperspectral estimation model of soil organic matter content based on principal gradient grey information

PurposeIn order to explore a new estimation approach of hyperspectral estimation, this paper aims to establish a hyperspectral estimation model of soil organic matter content with the principal gradient grey information based on the grey information theory.Design/methodology/approachFirstly, the estimation factors are selected by transforming the spectral data. The eigenvalue matrix of the modelling samples is converted into grey information matrix by using the method of increasing information and taking large, and the principal gradient grey information of modelling samples is calculated by using the method of pro-information interpolation and straight-line interpolation, respectively, and the hyperspectral estimation model of soil organic matter content is established. Then, the positive and inverse grey relational degree are used to identify the principal gradient information quantity of the test samples corresponding to the known patterns, and the cubic polynomial method is used to optimize the principal gradient information quantity for improving estimation accuracy. Finally, the established model is used to estimate the soil organic matter content of Zhangqiu and Jiyang District of Jinan City, Shandong Province.FindingsThe results show that the model has the higher estimation accuracy, among the average relative error of 23 test samples is 5.7524%, and the determination coefficient is 0.9002. Compared with the commonly used methods such as multiple linear regression, support vector machine and BP neural network, the hyperspectral estimation accuracy of soil organic matter content is significantly improved. The application example shows that the estimation model proposed in this paper is feasible and effective.Practical implicationsThe estimation model in this paper not only fully excavates and utilizes the internal grey information of known samples with “insufficient and incomplete information”, but also effectively overcomes the randomness and grey uncertainty in the spectral estimation. The research results not only enrich the grey system theory and methods, but also provide a new approach for hyperspectral estimation of soil properties such as soil organic matter content, water content and so on.Originality/valueThe paper succeeds in realizing both a new hyperspectral estimation model of soil organic matter content based on the principal gradient grey information and effectively dealing with the randomness and grey uncertainty in spectral estimation.

Thermal Energy-Kinetic Energy Conversion Efficiency Analysis Method Based on Grey Cluster Analysis

At this stage, although thermal power generation is still the main method of power generation in my country’s power plants, due to more and more energy consumption, the reserves of resources are gradually beginning to be stretched. Application in the power generation and production process of thermal power plants has become a road that more and more power plants must explore for development. In this paper, through the grey cluster analysis, the power plant can fully improve the power generation efficiency of the power plant in the process of utilizing the existing energy, so as to achieve the purpose of energy saving and environmental protection. Combined with the connotation of thermal energy-kinetic energy conversion, this paper establishes a theoretical framework for the analysis method of thermal energy-kinetic energy conversion efficiency based on grey cluster analysis. On the basis of theoretical research, using descriptive statistical tools, the main performance of thermal energy-kinetic energy conversion to promote economic growth is summarized. The realization method of gray cluster analysis theory is to identify and process the external features of gray information, capture effective information, and quantify the features (ie, whitening process), so as to complete the evaluation or decision-making, and verify the effect of these five mechanisms on economic growth. The final results of the study show that when K=1, 2, 3 and 4, the proportion of heat transfer is 41.63%, 42.19%, 49.63% and 52.13%, and the proportion of heat convection is 37.56% and 41.33%, respectively., 29.69% and 26.78%, the heat transfer and heat convection have the greatest influence on the thermal energy-kinetic energy conversion efficiency.

A novel hybrid grey-BCM approach in multi-criteria decision making: An application in OTT platform

Real world decision making is like a puzzle having complex, uncertain and vague information and this fact portray the wide range applicability of grey system theory in decision making procedure as grey system theory deals with the systems having information with uncertainty. In order to extend the base-criterion method to uncertain conditions, grey information may be a better way to solve a lot of multi-criteria decision-making problems. In this paper, we proposed a novel approach ‘grey base-criterion method’ (GBCM) based on the linguistic variables extended to the grey information. Weights of criteria have been calculated using GBCM. Numerical examples are illustrated and then the results are compared by the grey best-worst method (GBWM). Results of comparison show the high reliability of GBCM method with less consistency ratio over GBWM. A real case study of the fastest growing OTT (Over the Top) platforms in India has been taken to bestow the robustness of the proposed method.

DYNAMIC MULTI-ATTRIBUTE EVALUATION OF DIGITAL ECONOMY DEVELOPMENT IN CHINA: A PERSPECTIVE FROM INTERACTION EFFECT

This study aims to reflect the grey information coverage and complex interactions effect in digital economy development. Therefore, a multi-attribute decision making method based on the grey interaction relational degree of the normal cloud matrix (GIRD-NCM) model is proposed. First, the original information coverage grey numbers are transformed into normal cloud matrixes, and then a novel Minkowski distance between normal clouds is proposed by using different information principles. Second, the GIRD-NCM model is established according to the Choquet fuzzy integral and grey relational degree. Finally, the dynamic comprehensive evaluation of digital economy development in China from 2013 to 2020 is conducted. The implementation, availability, and feasibility of the GIRD-NCM model are verified by comparative analysis with three existing evaluation models. The empirical findings reveal a stable growth trend in China’s digital economy, with an annual growth rate of 7.87%, however, there are notable regional development disparities. The change in interaction degree has no effect on the rankings of provinces that are in the lead or have a moderately high level of digital economy development, but has a positive and negative impact on the rankings of these provinces with high and low levels of digital economy development, respectively.

Crop acreage planning for economy- resource- efficiency coordination: Grey information entropy based uncertain model

Reasonable adjustment of crop acreage can improve the sustainability of agriculture in arid zones by promoting economic development, alleviating water scarcity, and improving resource utilization efficiency but meets the challenges of various decision-maker preferences and uncertainties. With the objectives of maximizing economic benefits, minimizing evapotranspiration, and maximizing water productivity and water production efficiency, this study developed a grey entropy based multi-objective fuzzy constraint interval programming model (GEMOFCIP) through incorporation of information entropy technology, fuzzy constraints and interval programming approaches within a general optimization framework. The model is capable of (1) generating the crop acreage management schemes through trading off the different development goals; (2) dealing with the structure and parameter uncertainties of the optimization model; (3) measuring the relative importance of multiple objectives by the introduction of information entropy with the insufficient decision-maker’s preference information. Taking the crop acreage planning problem in Liangzhou, northwest China as a case study, the model was solved under different water-saving and satisfaction degree scenarios. It turned out that, after optimization, the economic benefit, water productivity and water production efficiency have been significantly improved, while the total ET and the total irrigation water have been significantly compressed in the study area. The GEMOFCIP model is superior to the previous models in that it takes into account the randomness of the relative importance of system objective, the vagueness of system constraints and the fluctuations of system coefficients, which would make the model more reasonable, stable and objective. Optimal results can help manage crop pattern with the economy-resource-efficiency consideration and accelerate the healthy development of agriculture in arid zones.

Gradient-assisted deep model for brain tumor segmentation by multi-modality MRI volumes

In clinical diagnosis, doctors make treatment strategies through key factors such as the location, shape and size of tumors. With the development of computer technology, the advantages of deep learning in medical image segmentation are increasingly prominent. Gliomas have the characteristics of diffuse infiltration, blurred boundary and swelling of brain tissue in affected areas, which makes it challenging to accurately segment brain tumors near the intersection area. The existing algorithms have achieved great results in segmenting tumors in terms of gray information. However they ignored the gradient of tumor boundary areas. The complexity of multi-modality MRI and the vast differences between brain tumor areas make it difficult to segment brain tumors efficiently and accurately. To solve the above problems, we propose a gradient-assisted multi-category brain tumor segmentation method(GAM-Net). GAM-Net includes three branches: (1) double convolutional encoder, which could capture rich features from multi-modality MRI; (2) gradient extraction branch, which could generate gradient features to assist area segmentation; and (3) gradient-driven decoder which could provide fusion contour information and encoding features effectively. We evaluated the effectiveness of the proposed algorithm on BraTS2020 dataset, of which 295 cases are used as training sets and 74 cases as test sets. Finally, the Dice Similarity Coefficients (DSC) of the proposed algorithm in whole tumor (WT), tumor core (TC), and enhanced tumor (ET) are 0.8991, 0.8402, and 0.7580 respectively. Average DSC reaches 0.8324. Experimental results show that GAM-Net can be successfully applied to segment brain tumors and thus helpful in diagnosis and treatment.

Remote-sensing image data fusion processing technology based on multi-level fuzzy judgment

Remote sensing image technology is of great significance for dynamic management and monitoring of ground buildings. In order to improve the data fusion ability of remote sensing image of ground buildings, a data fusion method of remote sensing image of ground buildings based on multi-level fuzzy evaluation is proposed. This method constructs a remote sensing image acquisition model of ground buildings, and uses image enhancement methods to realize the gray information analysis and image enhancement of the remote sensing image rate of ground buildings. Finally, combining the remote sensing image data fusion method and the fuzzy region reconstruction method, it reconstructs the pixels of the dynamically changed ground buildings. The simulation results show that the remote sensing image data fusion accuracy of ground buildings is good, and the remote sensing feature extraction accuracy of ground buildings is high. The dynamic real-time monitoring of remote sensing image of ground buildings is realized.

Probabilistic accumulation grey forecasting model and its properties

In grey system theory, the performance of an established grey forecasting model largely depends on how efficiently the grey information can be measured from data. Most existing grey forecasting models use grey accumulating generators to obtain grey information. However, not all grey information helps improve the performance of grey forecasting models. Consequently, simple accumulation leads to the accumulation of error from invalid grey information, which deteriorates the model performance. To address this issue, valid and invalid grey information were defined in this study according their contributions to the model performance. Further, it was proposed to use a Bernoulli distribution to simulate the distribution of valid/invalid grey information. Based on that probability, a probabilistic accumulation operator was designed to compute grey information, and this grey information was utilized to establish a probabilistic accumulation operator-based grey forecasting model (PGM(1,1)). PGM(1,1) is advanced in terms of forecasting because it is not negatively affected by invalid grey information. PGM(1,1) was tested on five public datasets and compared to other grey models as well as a wide range of other forecasting models, achieving state-of-the-art performance. In addition, a comprehensive analysis was conducted to demonstrate how the forecasting performance benefits from identifying valid/invalid grey information in PGM(1,1).

The robustness of complex product development projects under design change risk propagation with gray attack information

Disruptive design change risk propagation is a key obstacle restricting the success of complex product development (CPD) projects. Considering the incomplete information of initial change source selection, this paper develops the design change risk propagation model with gray attack information based on complex network theory. In this model, parameters π and σ are introduced to describe the gray information from both global and local perspectives. Based on the gray information, four kinds of gray attack strategies are provided. The robustness of the CPD project against design change risk propagation under different gray attacks is analyzed through numerical simulation. The results show that the CPD project is most robust under the small out-degree attack, while is most vulnerable under the large out-degree attack. No matter based on global or local gray information, increasing the gray level of information is beneficial to improve the project robustness under large out-degree attack, while reduces the project robustness under small out-degree attack under certain circumstances. Parameters σ and π have thresholds σ* and π*, which can keep the project robustness stable. This paper has implications for project managers to make reasonable decisions for project information management and provides a new perspective for CPD project robustness prediction and improvement.

Enhancing the depth perception of DSA images with 2D-3D registration.

Today, cerebrovascular disease has become an important health hazard. Therefore, it is necessary to perform a more accurate and less time-consuming registration of preoperative three-dimensional (3D) images and intraoperative two-dimensional (2D) projection images which is very important for conducting cerebrovascular disease interventions. The 2D-3D registration method proposed in this study is designed to solve the problems of long registration time and large registration errors in 3D computed tomography angiography (CTA) images and 2D digital subtraction angiography (DSA) images. To make a more comprehensive and active diagnosis, treatment and surgery plan for patients with cerebrovascular diseases, we propose a weighted similarity measure function, the normalized mutual information-gradient difference (NMG), which can evaluate the 2D-3D registration results. Then, using a multi-resolution fusion optimization strategy, the multi-resolution fused regular step gradient descent optimization (MR-RSGD) method is presented to attain the optimal value of the registration results in the process of the optimization algorithm. In this study, we adopt two datasets of the brain vessels to validate and obtain similarity metric values which are 0.0037 and 0.0003, respectively. Using the registration method proposed in this study, the time taken for the experiment was calculated to be 56.55s and 50.8070s, respectively, for the two sets of data. The results show that the registration methods proposed in this study are both better than the Normalized Mutual (NM) and Normalized Mutual Information (NMI). The experimental results in this study show that in the 2D-3D registration process, to evaluate the registration results more accurately, we can use the similarity metric function containing the image gray information and spatial information. To improve the efficiency of the registration process, we can choose the algorithm with gradient optimization strategy. Our method has great potential to be applied in practical interventional treatment for intuitive 3D navigation.

Infrared Small Target Detection via Center-Surround Gray Difference Measure With Local Image Block Analysis

Exristing algorithms may suffer from high false alarm rate and low detection probability when detecting dim small target under intricate background clutters and heavy noise. To address this problem, a target detection method based on local image block analysis and center-surround gray difference measure (CGDM) is proposed in this article. First, the infrared image is decomposed into a group of local blocks. The gray information of each block are sorted in ascending order and the last element is deleted, then the maximum gray jump point (MGJP) is extracted by using differential operation. Second, a protection zone is formed with MGJP as the center, and the maximal intensity of protection zone (MIPZ) is extracted. Next, by comparing the gray intensity of MGJP and MIPZ values, the potential target blocks can be reliably selected, while most background blocks and noise blocks will be discarded. After that, the CGDM is presented to enhance the target and suppress background clutters, and then, the center-surround local contrast measure (CLCM) is designed to further suppress the high-intensity clutter residues by searching the target center and revising the protection zone. Finally, the weighted center-surround gray difference measure (WCGDM) is defined by CLCM WCGDM map to recognize real targets. Extensive experiments show that the proposed method outperforms several existing algorithms in small target detection under complex background clutters, and it is robust to various target shapes, target sizes, and noise types.

Visual measurement method for three-dimensional shape of underwater bridge piers considering multirefraction correction

Underwater structure inspection faces great challenges because of the poor measuring environment. To conduct health monitoring for underwater bridge piers, this paper describes a method mainly composed of multicamera digital image correlation, a multirefraction correction model and an approach of grayscale mapping. Verification tests were carried out to examine the accuracy of the reconstruction results both in air and water, and the effectiveness of the multirefraction correction model was also proven. An underwater measuring system was built and was successfully applied to measure the round surface of a concrete pier underwater. The axial measurement error of the points reconstructed after multirefraction modification under the cylindrical coordinate system was only 1.58% of the diameter of the bridge pier. Furthermore, the gray information was transferred to the three-dimensional point cloud. This paper may promote the detection efficiency and accuracy of underwater bridge pier health monitoring.

Casualty scheduling optimisation with facility disruptions under grey information in early stage of post-earthquake relief

PurposeThe purpose of this paper is to propose a new casualty scheduling optimisation problem and to effectively treat casualties in the early stage of post-earthquake relief.Design/methodology/approachDifferent from previous studies, some new characteristics of this stage are considered, such as the grey uncertainty information of casualty numbers, the injury deterioration and the facility disruption scenarios. Considering these new characteristics, we propose a novel casualty scheduling optimisation model based on grey chance-constrained programming (GCCP). The model is formulated as a 0–1 mixed-integer nonlinear programming (MINP) model. An improved particle swarm optimisation (PSO) algorithm embedded in a grey simulation technique is proposed to solve the model.FindingsA case study of the Lushan earthquake in China is given to verify the effectiveness of the model and algorithm. The results show that (1) considering the facility disruption in advance can improve the system reliability, (2) the grey simulation technology is more suitable for dealing with the grey uncertain information with a wider fluctuation than the equal-weight whitening method and (3) the authors' proposed PSO is superior to the genetic algorithm and immune algorithm.Research limitations/implicationsThe casualty scheduling problem in the emergency recovery stage of post-earthquake relief could be integrated with our study to further enhance the research value of this paper.Practical implicationsConsidering the facility disruption in advance is beneficial to treat more patients. Considering the facility disruption in the design stage of the emergency logistics network can improve the reliability of the system.Originality/value(1) The authors propose a new casualty scheduling optimisation problem based on GCCP in the early stage of post-earthquake relief. The proposed problem considers many new characteristics in this stage. To the best of the authors' knowledge, the authors are the first to use the GCCP to study the casualty scheduling problem under the grey information. (2) A MINP model is established to formulate the proposed problem. (3) An improved integer-encoded particle swarm optimisation (PSO) algorithm embedded grey simulation technique is designed in this paper.

Prevalence of knowledge on maternal physical activity among pregnant women: a protocol for a systematic review

BackgroundMaternal physical activity is beneficial to pregnant women, fetus and newborns. Evidence suggests that the level of physical activity in this group is not up to the expectation around the world. Lack of knowledge on the benefits of physical activity during pregnancy and not being aware of the recommendations are major reasons for this situation. Although individual studies have reported various levels of knowledge on maternal physical activity in different populations, no studies have systematically reviewed the literature to provide global evidence on the topic that is useful in initiating multinational approaches to improve maternal physical activity. The proposed study aims to systematically review prevalence of knowledge on maternal physical activity among pregnant women in different regions in the world.MethodsThe proposed systematic review is designed according to the guidelines for conducting systematic reviews of prevalence and will be reported following the recommendations in the PRISMA statement. Quantitative cross-sectional, descriptive and observational studies published from year 2000 to 2022 will be included in the review. PubMed, Scopus, CINAHL, Embase, SPORTDiscus and Web of Science Core Collections will be searched using keywords relevant to physical activity, pregnant women and knowledge. Grey literature on the topic will be located through searching grey information sources, hand searching of reference lists and communicating with experts in the field. Screening of search results, selection and quality assessment of studies and data extraction will be independently performed by two reviewers. Assistance of a third reviewer will be sought to resolve any disagreement during the selection and quality assessment steps. After appraising the quality and consistency of selected studies, a premade data collection form will be used for data extraction. Narrative synthesis approach will be used in this review to analyze the evidence in primary studies.DiscussionThe proposed review will summarize evidence on the level of knowledge on maternal physical activity among pregnant women in different populations and delineate interregional discrepancies. The study will locate high priority regions with poor knowledge and identify elements of knowledge that needs attention.

True2 Orthoimage Map Generation

Digital/true orthoimage maps (D/TOMs) are one of the most important forms of national spatial data infrastructure (NSDI). The traditional generation of D/TOM is to orthorectify an aerial image into its upright and correct position by deleting displacements on and distortions of imagery. This results in the generated D/TOM having no building façade texture when the D/TOM superimposes on the digital building model (DBM). This phenomenon is no longer tolerated for certain applications, such as micro-climate investigation. For this reason, this paper presents the generation of a true2 orthoimage map (T2OM), which is radically different from the traditional D/TOM. The basic idea for the T2OM generation of a single building is to orthorectify the DBM-based building roof from up to down, the building façade from front to back, from back to front, from left side to right side, and from right side to left side, as well as complete a digital terrain model (DTM)-based T2OM, of which a superpixel is proposed to store building ID, texture ID, the elevation of each pixel, and gray information. Two study areas are applied to verify the methods. The experimental results demonstrate that the T2OM not only maintains the traditional characteristics of D/TOM, but also displays building façade texture and three-dimensional (3D) coordinates (XYZ) measurable at any point, and the accuracy of 3D measurement on a T2OM can achieve 0.025 m (0.3 pixel).