Sensor Characteristics Research Articles

Unlocking Novel Functionality: Pseudocapacitive Sensing in MXene-Based Flexible Supercapacitors

Extensively explored for their distinctive pseudocapacitance characteristics, MXenes, a distinguished group of 2D materials, have led to remarkable achievements, particularly in the realm of energy storage devices. This work presents an innovative Pseudocapacitive Sensor. The key lies in switching the energy storage kinetics from pseudocapacitor to electrical double layer capacitor by employing the change of local pH (-log[H+]) in MXene-based flexible supercapacitors during bending. Pseudocapacitive sensing is observed in acidic electrolyte but absent in neutral electrolyte. Applied shearing during bending causes liquid-crystalline MXene sheets to increase in their degree of anisotropic alignment. With blocking of H+ mobility due to the higher diffusion barrier, local pH increases. The electrochemical energy storage kinetics transits from Faradaic chemical protonation (intercalation) to non-Faradaic physical adsorption. We utilize the phenomenon of capacitance change due to shifting energy storage kinetics for strain sensing purposes. The developed highly sensitive Pseudocapacitive Sensors feature a remarkable gauge factor (GF) of approximately 1200, far surpassing conventional strain sensors (GF: ~ 1 for dielectric-cap sensor). The introduction of the Pseudocapacitive Sensor represents a paradigm shift, expanding the application of pseudocapacitance from being solely confined to energy devices to the realm of multifunctional electronics. This technological leap enriches our understanding of the pseudocapacitance mechanism of MXenes, and will drive innovation in cutting-edge technology areas, including advanced robotics, implantable biomedical devices, and health monitoring systems.

Investigation of a flexible, room-temperature fiber-shaped NH3 sensor based on PANI-Au-SnO2.

A sensitive compound was successfully obtained by coating polyaniline (PANI) on the surface of composite nanoparticles consisting of Au-loaded tin dioxide, named as PANI-Au-SnO2, using an in situ polymerization method. NH3 sensors in thin-film and fiber-shaped forms were prepared by inkjet printing and impregnation methods, respectively, based on PANI-Au-SnO2. The response characteristics of these NH3 sensors developed from composite sensitive materials were investigated in detail. Results indicate an effective response of the sensors to NH3 at room temperature. The thin-film sensor demonstrated a good linear relationship between the resistance change and NH3 concentration within the range of 5-40 ppm, indicating its excellent repeatability and long-term stability. In comparison to the thin film sensor, the fiber-shaped sensor showed a consistently stable response to NH3 even after 1000 cycles of repeated bending deformation. To demonstrate the practical application of the flexible fiber-shaped NH3 sensor, a cap designed for NH3 detection was fabricated by integrating the as-prepared sensor with a circuit board and an LED digital display. This assembly was incorporated into a commercially available ducktail cap, resulting in a wearable device capable of dynamically monitoring environmental NH3 levels and displaying real-time values. This innovative application underscores the potential of these sensors in real-world scenarios, particularly in occupational safety, where workers might be exposed to harmful levels of NH3. The cap could serve as a personal safety device, alerting the wearer to hazardous concentrations of NH3, which is particularly relevant in industrial or agricultural settings.

SEA++: Multi-Graph-Based Higher-Order Sensor Alignment for Multivariate Time-Series Unsupervised Domain Adaptation.

Unsupervised Domain Adaptation (UDA) methods have been successful in reducing label dependency by minimizing the domain discrepancy between labeled source domains and unlabeled target domains. However, these methods face challenges when dealing with Multivariate Time-Series (MTS) data. MTS data typically originates from multiple sensors, each with its unique distribution. This property poses difficulties in adapting existing UDA techniques, which mainly focus on aligning global features while overlooking the distribution discrepancies at the sensor level, thus limiting their effectiveness for MTS data. To address this issue, a practical domain adaptation scenario is formulated as Multivariate Time-Series Unsupervised Domain Adaptation (MTS-UDA). In this paper, we propose SEnsor Alignment (SEA) for MTS-UDA, aiming to address domain discrepancy at both local and global sensor levels. At the local sensor level, we design endo-feature alignment, which aligns sensor features and their correlations across domains. To reduce domain discrepancy at the global sensor level, we design exo-feature alignment that enforces restrictions on global sensor features. We further extend SEA to SEA++ by enhancing the endo-feature alignment. Particularly, we incorporate multi-graph-based higher-order alignment for both sensor features and their correlations. Extensive empirical results have demonstrated the state-of-the-art performance of our SEA and SEA++ on six public MTS datasets for MTS-UDA.

Biomechanical sensor signal analysis based on machine learning for human gait classification

Abstract The present study investigates the effect of wearable sensor placements and the use of various machine learning (ML) algorithms for human gait pattern recognition based on temporal gait speeds using wearable multichannel sensor data. Therefore, classifying human gait from features extracted from biomechanical sensor signals and evaluating the effect of using these sensors on gait biomechanics can be successfully achieved with a machine learning approach. In this study, firstly, IMU (Inertial Measurement Unit) and GON (Goniometer) sensor features were extracted for machine learning input using the sliding windows method, and these features were applied to ML classifiers to classify human gait speed. Our experimental findings show that multi- and fusion sensor models provide superior classification performance compared to single sensor models, and the highest accuracy is achieved with the ‘FUS09’ fusion sensor model and SVM-based classifier. The classification accuracies of the FUS10 fusion sensor model, where all sensor parameters were combined, the FUS09 model, where the GON_ANK and IMU_Ft parameters in this model were excluded, are 0.895 and 0.901, respectively. Consequently, wearable biomechanical sensor data and machine learning approach can be easily preferred in multiple human activity recognition.

A random encounter model for wildlife density estimation with vertically oriented camera traps

AbstractThe random encounter model (REM) estimates animal densities from camera‐trap data by correcting capture rates for a set of biological variables of the animals (average group size, speed and activity level) and characteristics of camera sensors. The REM has been widely used for setups in which cameras are mounted on trees or other structures aimed parallel to the ground. Here, we modify the REM formula to accommodate an alternative field of view acquired with vertically oriented camera traps, a type of deployment used to avoid camera theft and damage. We show how the calculations can be adapted to account for a different detection zone with minor modifications. We find that the effective detection area can be close to a rectangle with dimensions influenced by the properties of the Fresnel lens of the camera's motion sensor, the body mass of different species and the height of the camera. The other REM parameters remain the same. We tested the modified REM (vREM) by applying it to wildlife data collected with vertically oriented camera traps in Bardia National Park, Nepal. We further validated that the effective detection area for the camera model used was best approximated as a rectangle shape using maximum likelihood estimation. Density estimates obtained broadly matched independent density estimates for nine species from the previous studies in Bardia with varying body sizes by four orders of magnitude. We conclude that these modifications allow the REM to be effectively used for mammal density estimation for species with a wide range of body sizes, with vertically oriented camera traps.

Design of fluorescent sensors for the detection of human serum albumin structure employing nitrogen-by modulation of molecular containing heterocycles

Serum albumin is the most abundant protein in mammalian plasma. It occupies an extremely important position in organisms as an early indicator of the development of many diseases. The development of fluorescence sensing technology has revolutionized the drawbacks of traditional methods. In this strategy, we designed and synthesized three dual fluorophore organic sensors TPA-IPA-T1, TPA-QL-T2, and TPA-Py-T3 for the detection of serum albumin. The rotation of the C–C bond of sensors is restricted and the molecular structure forms a rigid plane that contributes to the fluorescent emission intensity of the sensor. The fluorescent skeletons were successfully constructed by triphenylamine composed the electron-withdrawing nitrogen-containing compounds benzopyrrole (indole), benzopyridine (quinoline), and pyridine, respectively, with piperazine as a rigid connecting bridge. The addition of human serum albumin (HSA) turned on the fluorescent emission of the sensor near 580–600 nm. The color of the sensor solution changed from colorless to pink and bright orange, respectively. The fluorescent intensity increased by 24, 67, and 31 fold, respectively. The sensors have the advantages of sensitivity and fast response, strong anti-interference ability, low detection limit and good water solubility. Drug experiments in artificial urine demonstrate the characteristics of sensors, which has a very high potential for biological feasibility and medical applications. The sensors facilitaty the binding of the HSA through electrostatic interactions. The sensors were explored to be able to efficiently bind the IB structural domain of HSA, which had a good meaningful of avoiding the interference of commonly drugs in clinical medicine and in artificial urine on the detection of HSA.

Gas–liquid two-phase flow measurement by using electrical tomography sensors and Venturi

Gas–liquid two-phase flow is characterized by complexity, non-homogeneity and difficulty in modeling. Therefore, traditional measurement methods often struggle to simultaneously meet the requirements of high precision and a wide measurement range. In this context, this study aims to explore an innovative multi-sensor fusion technique to achieve accurate measurement of gas–liquid two-phase flow under full operating conditions. In this study, a combination of three sensors, Venturi, ERT (Electrical Resistance Tomography) and ECT (Electrical Capacitance Tomography), is used to propose a new method based on the fusion of LVF (Liquid-phase Volume Fraction) calculations with flow results. Firstly, the high sensitivity characteristics of ECT and ERT sensors are utilized to obtain spatial distribution information of the flow, which is then used to calculate the LVF. Subsequently, the calculated LVF is combined with the total flow rate measured by the Venturi. Through an established mathematical model, the individual flow rates of the gas and liquid phases are inversely calculated. The results show that the method exhibits high measurement accuracy in different LVF ranges with the relative error controlled within 10%, especially in wet and non-wet gas conditions, which fills the gap of the traditional methods in this field. In addition, the method not only improves the measurement accuracy, but also broadens the measurement range, which meets the diversified needs of complex two-phase flow measurement in the oil and gas industry.

Evaluation of Electrical Characteristics of Weft-Knitted Strain Sensors for Joint Motion Monitoring: Focus on Plating Stitch Structure.

We developed a sensor optimized for joint motion monitoring by exploring the effects of the stitch pattern, yarn thickness, and NP number on the performance of knitted strain sensors. We conducted stretching experiments with basic weft-knit patterns to select the optimal stitch pattern and analyze its sensitivity and reproducibility. The plain stitch with a conductive yarn located on the reverse side exhibited the highest gauge factor value (143.68) and achieved excellent performance, with a stable change in resistance even after repeated sensing. For an in-depth analysis, we developed six sensors using the aforementioned pattern with different combinations of yarn thickness (1-ply, 2-ply) and NP numbers (12, 13, 14). Based on bending experiments, the GF across all sensors was 60.2-1092, indicating noticeable differences in sensitivity. However, no significant differences were observed in reproducibility, reliability, and responsiveness, confirming that all the sensors are capable of joint motion monitoring. Therefore, the plain-patterned plating stitch structure with conductive yarn on the reverse side is optimal for joint motion monitoring, and the yarn thickness and NP numbers can be adjusted to suit different purposes. This study provides basic data for developing knitted strain sensors and offers insights into how knitting methods impact sensor performance.

Novel deep learning based soft sensor feature extraction for part weight prediction in injection molding processes

In the current injection molding (IM) industry, it remains challenging to monitor and estimate production quality promptly. It is costly and time-consuming to measure part quality manually after each production cycle ends, which results in quality defects difficult to be captured in time. In this case, a soft sensor is essential to model the IM process and predict the final quality in real time with multi-source industrial production data. However, traditional data-driven modeling methods fail to take advantage of the information in complex high-frequency data from in-mold sensors, resulting in an inaccurate IM model and unsatisfactory quality prediction performance. To solve this problem, this paper proposes a novel soft sensor framework based on a teacher-student structure. After specialized preprocessing of multiple sensor time series data, a GRU-based autoencoder with an attention mechanism (GRU-A-AE) is trained as a teacher model, extracting deep implicit features involving valuable time sequential information. Then, a cascaded relationship among shallow feature points from sensor signals, deep features, and final part weights is established using back propagation neural networks (BPNNs). To demonstrate its effectiveness and superiority, the proposed soft sensor is trained and tested with practical IM data under normal and fluctuating production conditions, respectively. Compared with conventional methods, our method has higher prediction accuracy with testing RMSE of 0.1049 and R2 of 0.9950 under normal conditions, which proves more valuable information in high-frequency sensor signals are explored from the teacher model and IM production dynamics are captured precisely. In addition, its better prediction performance in the case of production condition fluctuation verifies its strong robustness.

Rhodamine B‐Modified Nanocrystalline Cellulose as Fluorescent Sensor for Fe3+ Ion Detection

AbstractIron is crucial for various biological processes in humans, animals, and plants, making precise and efficient monitoring of iron ion concentrations essential. To address this need, a cellulose‐based smart fluorescent material (NCC@NH2@RB) with a conjugated structure is designed and synthesized for the rapid and sensitive detection of Fe3+ ions. This sensor features core‐shell nanoparticles functionalized with rhodamine B (RB), using (3‐aminopropyl)triethoxysilane (APTES) as a linker. The resulting fluorescent probe, composed of nanocrystalline cellulose and RB, exhibits strong fluorescence under ultraviolet light, making it an effective tool for Fe3+ ion detection in water. When Fe3+ ions are introduced into a solution containing NCC@NH2@RB, they bind with weak‐field ligands such as nitrogen and oxygen in the probe's structure, forming complexes. This interaction involves high‐spin coordination and leads to the self‐assembly of Fe3+ ions on the surface of NCC@NH2@RB. The process generates single electrons, increasing paramagnetism and quenching the fluorescence. The NCC@NH2@RB fluorescent probe has a limit of detection (LOD) of 0.01 µM and a limit of quantification (LOQ) of 0.03 µM with linearity at concentrations of 1.0 × 10−4–2.5 × 10−3 m. This fluorescence quenching effect is specific to Fe3+ ions, ensuring that the probe remains unaffected by other metal ions.

ANALYSIS OF THE CHARACTERISTICS OF AUTOMOTIVE MASS AIR FLOW SENSORS

An analysis of the characteristics of automotive mass air flow sensors is made. A test system has been developed to study the sensors. The measurement data is processed with MATLAB using curve fitting.

Animal-Morphing Bio-Inspired Mechatronic Systems: Research Framework in Robot Design to Enhance Interplanetary Exploration on the Moon.

Over the past 50 years, the space race has potentially grown due to the development of sophisticated mechatronic systems. One of the most important is the bio-inspired mobile-planetary robots, actually for which there is no reported one that currently works physically on the Moon. Nonetheless, significant progress has been made to design biomimetic systems based on animal morphology adapted to sand (granular material) to test them in analog planetary environments, such as regolith simulants. Biomimetics and bio-inspired attributes contribute significantly to advancements across various industries by incorporating features from biological organisms, including autonomy, intelligence, adaptability, energy efficiency, self-repair, robustness, lightweight construction, and digging capabilities-all crucial for space systems. This study includes a scoping review, as of July 2024, focused on the design of animal-inspired robotic hardware for planetary exploration, supported by a bibliometric analysis of 482 papers indexed in Scopus. It also involves the classification and comparison of limbed and limbless animal-inspired robotic systems adapted for movement in soil and sand (locomotion methods such as grabbing-pushing, wriggling, undulating, and rolling) where the most published robots are inspired by worms, moles, snakes, lizards, crabs, and spiders. As a result of this research, this work presents a pioneering methodology for designing bio-inspired robots, justifying the application of biological morphologies for subsurface or surface lunar exploration. By highlighting the technical features of actuators, sensors, and mechanisms, this approach demonstrates the potential for advancing space robotics, by designing biomechatronic systems that mimic animal characteristics.

DC characteristics of a dynamic vision sensor's photoreceptor circuit evaluated for event-based sensing in the mid-wave infrared

Forthcoming infrared event-based sensors will have utility in the space-based surveillance domain where they could potentially perform traditional sensing and tracking functions with significantly enhanced temporal resolution and reduced downstream datalink demands and power consumption. As a first step toward extending event-based sensing technology into the mid-wave infrared, a DC simulation of the conventional event-based sensor unit cell's photoreceptor circuit is performed, and the results are compared with measurements of a printed circuit board implementation of the same circuit to assess what design freedom is available to interface the photoreceptor with a mid-wave infrared photodetector. Detailed analysis of the circuit and measurements provides insight into which fundamental properties of the transistors drive the photoreceptor's dynamic range and demonstrates several characteristics that are relevant to mid-wave infrared sensing. These characteristics include the capability to produce a stable, low voltage bias on the infrared photodetector to maximize sensitivity, and that operating the circuit below room temperature increases the photoreceptor's dynamic range. Measurements show that even the simplest implementation of the photoreceptor circuit exhibits a dynamic range of logarithmic compression of 150 dB at room temperature. However, the dynamic range is ultimately limited by the mid-wave infrared photodetector's dark current activation energy, which is significantly lower than the threshold voltage (energy) of the photoreceptor's feedback transistor and, thus, there is an incentive for the photodetector's dark current to be diffusion-limited.

Flat-field methods for color image sensors

The research provides a detailed analysis of the primary sources of non-uniformity in color CMOS image sensors. It introduces the fundamental theory of traditional photon conversion and the main objective and subjective quality evaluation metrics for images. The theoretical foundations of traditional correction methods are also discussed. Based on the photon response characteristics of the color image sensor, a method for flat-field correction of color image sensors is proposed by analyzing the grayscale response curves of each pixel. This method addresses the non-linearity issue in pixel photon response caused by crosstalk between pixels and improves the non-uniformity problem in pixel response.

Mitigating the directional retrieval error of solar-induced chlorophyll fluorescence in the red band

Solar-induced chlorophyll fluorescence (SIF) is a promising tool to estimate gross primary production (GPP), but the retrieval of SIF is commonly noisy and highly sensitive to various interference factors. Particularly, the retrieval of SIF in the red band (RSIF) is more challenging than in the far-red SIF (FRSIF) due to the weaker fluorescence signal and the weaker absorption depth of oxygen at the red band compared with the far-red band. A comprehensive evaluation of all factors will allow a reproducible interpretation of SIF signals and advance the estimation of GPP from SIF. Recent studies have assessed the sensitivity of SIF retrieval to sensor characteristics, retrieval methods, and hardware specifications. However, none of these studies have systematically investigated the directional retrieval error of SIF resulting from the mismatch between irradiance measured above the canopy and the true irradiance reaching the canopy components viewed by a sensor. This study illustrated the effect of mismatched irradiance on the retrieval of RSIF using the commonly used standard 3FLD method based on SCOPE model simulations. The retrieval accuracy was highest in the hotspot direction, but it decreased as the observation direction was away from the hotspot. The relative root mean square error (RRMSE) was generally higher than 20 % in the forward directions. To reduce the retrieval error due to the mismatch effect, we proposed a modified 3FLD method (MFLD) by calculating the true irradiance reaching the canopy in a given direction based on geometric optical theory. The results showed that MFLD clearly improved the retrieval accuracy for RSIF, especially in the forward directions where RRMSE decreased by 10 % in most cases. For example, the RRMSE was reduced from 19.26 % to 5.50 % after mitigating the mismatch between the measured and actual solar irradiance, when the solar zenith angle was 40° and viewing zenith angle was 30° in the forward solar principal plane. Even at the nadir observation, the RRMSE was also reduced from 12.84 % to 5.64 %. In summary, MFLD can effectively mitigate the irradiance mismatch effect on the retrieval of RSIF. These results will improve our interpretation of the relationship between GPP and RSIF at different observation directions.

Control of sensitivity in metal oxide electrolyte gated field-effect transistor-based glucose sensor by electronegativity modulation

In this study, the sensitivity of electrolyte-gated field-effect transistor-based glucose sensors using oxide semiconductor materials was controlled via electronegativity modulation. By controlling the enzymatic reaction between glucose and glucose oxidase, which is affected by the surface potential, the sensitivity of the glucose sensor can be effectively adjusted. To evaluate the sensitivity characteristics of the glucose sensor according to electronegativity control, devices were fabricated based on InO through Ga and Zn doping. The results confirmed that the specific sensitivity range could be adjusted by increasing the electronegativity. In addition, density functional theory calculations, confirmed that the attachment energy of the surface-functionalized material and the enzyme binding energy in the surface-functionalized thin film can be modulated depending on the electronegativity difference. The dissociation constant was controlled in both directions by doping with metal cations with larger(Ga, 1.81) or smaller(Zn, 1.65) electronegativities in InO(In, 1.78). We expect that this study will provide a simple method for the gradual and bidirectional control of the glucose sensitivity region.

Study on the Mechanism of Temperature Effect on SO2 Electrochemical Gas Sensor

Abstract Temperature can affect the measurement values of electrochemical gas sensors, increasing measurement errors. The influence mechanism of temperature on electrochemical gas sensors was studied based on Fick's first law and the limit diffusion current formula. Temperature affects the sensitive characteristics of sensor by changing the diffusion coefficient Dl1 of SO2 in air, the Henry's coefficient KH of SO2 dissolved in water and the water content of the electrolyte solution. When the temperature increases, the degree of influence of Henry's coefficient KH and the reduction of water content is greater than the degree of influence on the increase in diffusion coefficient, which decreases the sensor measurement value. The results of the temperature experiments show that the optimal temperature range for the sensor is -25 °C to 50 °C, and the average measurement error in this temperature range is less than 20%. When the temperature exceeds 50 ℃, it will cause a reduction in the evaporation of water in the electrolyte solution, leading to a rapid increase in the measurement error. The structure of the sensor can be improved by adding a water retention layer inside the sensor to supplement the electrolyte solution with water, so as to reduce the measurement error.

Exploring the Nexus between Sensor Reliability and System Performance

In contemporary technological landscapes, sensors play a pivotal role in enabling diverse applications across industries, from healthcare to manufacturing. This paper undertakes a thorough investigation on system performance (reliability and availability of a system), focusing on the critical interplay between baseline performance, performance with integrated sensors and performance considering sensor reliability, recognizing the foundational importance of sensors in data-driven decision-making processes. The research employs a causation-based approach to systematically develop functional relations within the system. The failures identified of each component and functional relationships will then be analyzed using a simulation technique to understand the inherent performance of the engineering system. From here, a genetic algorithm is used to design a sensor set and tailor it for an engineering system, providing a foundation for conducting trade studies in the paper's subsequent sections. Through rigorous quantitative analysis and simulations, we compare the impacts of the performance of the sensor set design compared to the baseline performance. The paper then investigates the complexities of sensor reliability on overall system performance. Through advanced simulations, we elucidate the potential cascading effects that variations in sensor reliability can have on the system's performance. By exploring these ripple effects, we aim to provide a comprehensive understanding of how sensor reliability plays a crucial role in determining the success of complex systems. Beyond the immediate considerations of sensor characteristics, the paper analyses the maintenance aspects of sensors by performing a series of analyses to suggest maintenance aimed at improving the sensor and hence system reliability. Highlighting the relationship between sensor reliability and system performance, this section stresses the critical role of consistent maintenance practices in ensuring sustained data quality and system functionality. In conclusion, this paper aims to highlight the different perspectives that can be analyzed to understand the reality of system performance, considering facets such as sensor maintenance and reliability. It also aims to demonstrate various approaches that can be applied to engineering systems to uncover truths about sensor performance and reliability.

Unsupervised multi-target domain adaptation for deforestation detection in tropical rainforest

Abstract. Geographic variability of the classes of interest, differences in sensor characteristics and changes in atmospheric conditions during image acquisition, among other factors, make it challenging to use a pre-trained deep learning classifier on new remote sensing data without a substantial drop in classification accuracy. This phenomenon occurs due to the so-called domain shift problem. Deep domain adaptation techniques have been used to mitigate the problem and thus avoid the time-consuming and costly collection of new labeled samples. Most recent domain adaptation approaches rely on single-source and single-target domains, refraining from exploiting other data distributions that are usually available. This work introduces a new unsupervised multi-target domain adaptation in the context of a change detection application, namely deforestation detection. The proposed approach addresses the substantial class imbalance typical of such application by applying unsupervised algorithms for selecting pseudo-labels in the target domain that will later serve as additional training references. We report results of experiments to evaluate the proposed method in four distinct sites of two Brazilian biomes using Sentinel-2 images. The results indicate that the proposed unsupervised domain adaptation method is a promising solution to reduce the effects of domain shift and to deal with the scarcity of labeled training data.

Relative Radiometric Correction Method Based on Temperature Normalization for Jilin1-KF02

The optical remote sensors carried by the Jilin-1 KF02 series satellites have an imaging resolution better than 0.5 m and a width of 150 km. There are radiometric problems, such as stripe noise, vignetting, and inter-slice chromatic aberration, in their raw images. In this paper, a relative radiometric correction method based on temperature normalization is proposed for the response characteristics of sensors and the structural characteristics of optical splicing of Jilin-1 KF02 series satellites cameras. Firstly, a model of temperature effect on sensor output is established to correct the variation of sensor response output digital number (DN) caused by temperature variation during imaging process, and the image is normalized to a uniform temperature reference. Then, the horizontal stripe noise of the image is eliminated by using the sensor scan line and dark pixel information, and the vertical stripe noise of the image is eliminated by using the method of on-orbit histogram statistics. Finally, the method of superposition compensation is used to correct the vignetting area at the edge of the image due to the lack of energy information received by the sensor so as to ensure the consistency of the image in color and image quality. The proposed method is verified by Jilin-1 KF02A on-orbit images. Experimental results show that the image response is uniform, the color is consistent, the average Streak Metrics (SM) is better than 0.1%, Root-Mean-Square Deviation of the Mean Line (RA) and Generalized Noise (GN) are better than 2%, Relative Average Spectral Error (RASE) and Relative Average Spectral Error (ERGAS) are greatly improved, which are better than 5% and 13, respectively, and the relative radiation quality is obviously improved after relative radiation correction.