Resolution Of Sensor Research Articles

Advancing Skyborne Technologies and High-Resolution Satellites for Pasture Monitoring and Improved Management: A Review

The timely and accurate quantification of grassland biomass is a prerequisite for sustainable grazing management. With advances in artificial intelligence, the launch of new satellites, and perceived efficiency gains in the time and cost of the quantification of remote methods, there has been growing interest in using satellite imagery and machine learning to quantify pastures at the field scale. Here, we systematically reviewed 214 journal articles published between 1991 to 2021 to determine how vegetation indices derived from satellite imagery impacted the type and quantification of pasture indicators. We reveal that previous studies have been limited by highly spatiotemporal satellite imagery and prognostic analytics. While the number of studies on pasture classification, degradation, productivity, and management has increased exponentially over the last five years, the majority of vegetation parameters have been derived from satellite imagery using simple linear regression approaches, which, as a corollary, often result in site-specific parameterization that become spurious when extrapolated to new sites or production systems. Few studies have successfully invoked machine learning as retrievals to understand the relationship between image patterns and accurately quantify the biophysical variables, although many studies have purported to do so. Satellite imagery has contributed to the ability to quantify pasture indicators but has faced the barrier of monitoring at the paddock/field scale (20 hectares or less) due to (1) low sensor (coarse pixel) resolution, (2) infrequent satellite passes, with visibility in many locations often constrained by cloud cover, and (3) the prohibitive cost of accessing fine-resolution imagery. These issues are perhaps a reflection of historical efforts, which have been directed at the continental or global scales, rather than at the field level. Indeed, we found less than 20 studies that quantified pasture biomass at pixel resolutions of less than 50 hectares. As such, the use of remote sensing technologies by agricultural practitioners has been relatively low compared with the adoption of physical agronomic interventions (such as ‘no-till’ practices). We contend that (1) considerable opportunity for advancement may lie in fusing optical and radar imagery or hybrid imagery through the combination of optical sensors, (2) there is a greater accessibility of satellite imagery for research, teaching, and education, and (3) developers who understand the value proposition of satellite imagery to end users will collectively fast track the advancement and uptake of remote sensing applications in agriculture.

Finite element-based feasibility study on utilizing heat flux sensors for early detection of vascular graft infections

Aortic vascular graft infections have high morbidity and mortality rate, however, patients often do not show symptoms. Continuous implant surface monitoring will allow for early detection of infections on implant surfaces, which allows for antibiotic treatment prior to biofilm formation. We explore the possibility of using heat flux sensors mounted on an aortic vascular graft to sense the localized heat production at the onset of infectious growth. We apply Finite Element Model simulations to demonstrate changes of the heat transfer coefficient depending on different pulsatile flow parameters. We determine various differences, the main influence being the distance travelled from the inlet of the simulation with the highest heat transfer coefficient closest to the inlet and decreasing along the direction of travel of the fluid. The determined range of heat transfer coefficients of 200 to 4800 W/m2 was applied to a second simulation of the thermal environment of the implant. We determined the heat transfer efficiency of the aortic graft system depending on different graft materials and thicknesses. We are further able to determine that the early detection of infection is possible by comparing the simulated amount of heat flux produced locally with the resolution of a commercial heat flux sensor.

Noise Suppression of Nitrogen-Vacancy Magnetometer in Lock-In Detection Method by Using Common Mode Rejection.

Nitrogen-vacancy (NV) centers in diamonds are promising solid-state magnetic sensors with potential applications in power systems, geomagnetic navigation, and diamond NV color center current transformers, in which both high bandwidth and high magnetic field resolution are required. The wide bandwidth requirement often necessitates high laser power, but this induces significant laser fluctuation noise that affects the detection magnetic field resolution severely. Therefore, enhancement of the magnetic field resolution of wide-bandwidth NV center magnetic sensors is highly important because of the reciprocal effects of the bandwidth and magnetic field resolution. In this article, we develop a common mode rejection (CMR) model to eliminate the laser noise effectively. The simulation results show that the noise level of the light-detected magnetic resonance signal is significantly reduced by a factor of 6.2 after applying the CMR technique. After optimization of the laser power and modulation frequency parameters, the optimal system bandwidth was found to be 75 Hz. Simultaneously, the system's detection magnetic field resolution was enhanced significantly, increasing from 4.49 nT/Hz1/2 to 790.8 pT/Hz1/2, which represents an improvement of nearly 5.7 times. This wide-bandwidth, high-magnetic field resolution NV color center magnetic sensor will have applications including power systems, geomagnetic navigation, and diamond NV color center current transformers.

Instrumented Flexible Glass Structure: A Bragg Grating Inscribed with Femtosecond Laser Used as a Bending Sensor.

Fused silica glass is a material with outstanding mechanical, thermal and optical properties. Being a brittle material, it is challenging to shape. In the last decade, the manufacturing of monolithic flexible mechanisms in fused silica has evolved with the femtosecond-laser-assisted etching process. However, instrumenting those structures is demanding. To address this obstacle, this article proposes to inscribe a Bragg Grating sensor inside a flexure and interface it with an optical fibre to record the strain using a spectrum analyser. The strain sensitivity of this Bragg Grating sensor is characterized at 1.2 pm/μϵ (1 μϵ = 1 microstrain). Among other applications, deformation sensing can be used to record a force. Its use as a micro-force sensor is estimated. The sensor resolution is limited by our recording equipment to 30 μN over a measurement range above 10 mN. This technology can offer opportunities for surgery applications or others where precision and stability in harsh environments are required.

Sentinel-2-based predictions of soil depth to inform water and nutrient retention strategies in dryland wheat

The thickness or depth of fine-textured soil (zf) dominates water storage capacity and exerts a control on nutrient leaching in semi-arid agroecosystems. At small pixel sizes (< 1 m; ‘fine resolution’), the normalized difference vegetation index (NDVI) of cereal crops during senescence (Zadoks Growth Stages [ZGS] 90–93) offers a promising alternative to destructive sampling of zf using soil pits. However, it is unclear whether correlations between zf and NDVI exist (a) at larger pixel sizes (1–10 m; ‘intermediate resolution’) and (b) across field boundaries. The relationship of zf to NDVI of wheat (Triticum aestivum L.) was tested using images from a combination of multispectral sensors and fields in central Montana. NDVI was derived for one field using sensors of fine and intermediate spatial resolution and for three fields using intermediate resolution sensors only. Among images acquired during crop senescence, zf was correlated with NDVI (p < 0.05) independent of sensor (p = 0.22) and field (p = 0.94). The zf relationship to NDVI was highly dependent on acquisition day (p < 0.05), but only when pre-senescence (ZGS ≤ 89) images were included in the analysis. Results indicate that cereal crop NDVI of intermediate resolution can be used to characterize zf across field boundaries if image acquisition occurs during crop senescence. Based on these findings, an empirical index was derived from multi-temporal Sentinel-2 imagery to estimate zf on fields in and beyond the study area.

Drone-based magnetometer prospection for archaeology

Magnetometry is one of the most efficient and successful methods of archaeological prospection. Drone-based prospecting is increasingly being used in many fields of remote sensing but with respect to magnetometry, with rather poor results. For magnetic surveys, drone prospecting comes with the problem that magnetic and mechanical disturbances originating from the aircraft decrease the quality of the measurements.Here we present the adaptation of a commercial three-axis fluxgate magnetometer setup, which can be suitable for archaeological prospection, after applying appropriate filter methods and minimising the flying altitude and speed. For approval of the system, we performed drone-based surveys in high spatial resolution (50 cm line spacing) at constant, ultra-low sensor altitudes of 45 ± 10 cm and 75 ± 10 cm. We chose an archaeological site from the roman period as survey site (3.8 ha), where high-quality ground-based caesium magnetometer data was available. This allows us to demonstrate the first detailed comparison of drone-based and ground-based magnetic survey data for archaeology. For further evaluation, the influence of the drone on the measurements is assessed, drift and sensor resolution checks are carried out, and suitable data filtering methods are evaluated.Our results show that we can detect main archaeological features such as ditches, pits, fireplaces and remnants of stone fundaments with the drone-based setup, which are the relevant structures for the majority of archaeological prospections worldwide. Further, we can prove that the data quality can significantly be increased by lowering the flight altitude and speed. We conclude that drone-based magnetometry serves above all to cover large and inaccessible areas in short time. Our findings are a first step towards the development of standards for drone-based archaeological prospection approaches.

Low-speed instrumented drill press for bone screw insertion

Screw insertion torque is a widely used/effective method for quantifying fixation strength in orthopedic implant research for different screw geometries, implantation sites, and loads. This work reports the construction of an open-source instrumented benchtop screw insertion device for a total cost of $7545 ($492 + $7053 for equipped sensors), as well as validation of the device and an example use-application. The insertion device is capable of recording the axial load, rotational speed, and applied torque throughout the screw insertion process at 10 samples per second, as demonstrated in the validation test. For this combination of bone analog (20 PCF Sawbones©), screw, and loading, the resolution of the torque sensor was 25% of the maximum measured torque; a different model torque sensor would be required to meet ASTM F543-17, which specifies a resolution of 10% of the maximum torque. This system is optimized for fastener insertion at speeds of 120 rpm or less and axial loading up to 50 N.

Learning Contours for Point Cloud Completion

The integrity of a point cloud frequently suffers from discontinuous material surfaces or coarse sensor resolutions. Existing methods focus on reconstructing the overall structure, but salient points or small irregular surfaces are difficult to be predicted. Toward this issue, we propose a new end-to-end neural network for point cloud completion. To avoid non-uniform point density, the regular voxel centers are selected as reference points. The encoder and decoder are designed with Patchify, transformers, and multilayer perceptrons. An implicit classifier is incorporated in the decoder to mark the valid voxels that are allowed for diffusion after removing vacant grids from completion. With newly designed loss function, the classifier is trained to learn the contours, which helps to identify the grids that are difficult to be judged for diffusion. The effectiveness of the proposed model is validated in the experiments on the indoor ShapeNet dataset, the outdoor KITTI dataset, and the airbone laser dataset by competing with state-of-the-art methods, which show that our method can predict more accurate point coordinates with rich details and uniform point distributions.

Refractive Index Sensitivity Characterization of Tilted Fiber Bragg Grating Based Surface Plasmon Resonance Sensor

We have systematically investigated the refractive index (RI) sensing characterization of tilted fiber Bragg grating (TFBG) based surface plasmon resonance (SPR) sensors in terms of period and tilt angle of grating. The numerical analysis has revealed that the RI sensitivity of TFBG-SPR is linearly proportional to the grating period, and slightly improved with increasing the tilt angle of grating. Under the same normal grating period, the TFBG-SPR sensors with different tilt angles have the different cladding mode order range, which is responding to the different sensing ranges. Furthermore, we have fabricated the TFBG-SPR sensors with different periods and different tilt angles (5°, 7°, 10°, 12°, and 15°). Experimental results show that the larger normal grating period of TFBG is, the higher RI sensitivity of the SPR sensor is, in which the 10° TFBG-SPR sensors with the normal grating period of 529.37nm, 680.20nm have the RI sensitivity of 547.6nm/RIU, 723.6nm/RIU, respectively. And the TFBG-SPR sensors with different tilt angles slightly affect the RI sensitivity, but the sensors with different tilt angles have different sensing ranges, in which the RI sensitivity of TFBG-SPR sensors with the tilt angle of 7°, 10°, 12° are 545.5 nm/RIU, 547.6 nm/RIU, 549.1 nm/RIU in the RI range of 1.38-1.42,1.33-1.40,1.33-1.37 respectively. Finally, we have discussed the sensing resolution of TFBG-SPR sensor which the larger tilt angle and longer period could worsen the sensing resolution of TFBG-SPR sensor.

Deep learning-based super-resolution for harmful algal bloom monitoring of inland water

ABSTRACTInland water frequently occurs during harmful algal blooms (HABs), rendering it challenging to comprehend the spatiotemporal features of algal dynamics. Recently, remote sensing has been applied to effectively detect the algal spatiotemporal behaviors in expensive water bodies. However, image sensor resolution limitation can render the understanding of spatiotemporal features of relatively small water bodies challenging. In addition, few studies have improved the resolution of remote sensing images to investigate inland water quality, owing to the image sensor resolution limitations. Therefore, this study applied deep learning-based Super-resolution for transforming satellite imagery of 20 m to airborne imagery of 5 m. After performing atmospheric correction for the acquired images, we adopted super-resolution (SR) methodologies using a super-resolution convolutional neural network (SRCNN) and super-resolution generative adversarial networks (SRGAN) to estimate the Chlorophyll-a (Chl-a) concentration in the Geum River of South Korea. Both methods generated SR images with water reflectance at 665, 705, and 740 nm. Then, two band-ratio algorithms at 665 and 740 nm wavelengths were applied to the reflectance images to estimate the Chl-a concentration maps. The SRCNN model outperformed SRGAN and bicubic interpolation with peak signal-to-noise ratios (PSNR), mean square errors (MSE), and structural similarity index measures (SSIM) for the validation dataset of 24.47 (dB), 0.0074, and 0.74, respectively. SR maps from the SRCNN provided more detailed spatial information on Chl-a in the Geum River compared to the information obtained from satellite images. Therefore, these findings showed the potential of deep learning-based SR algorithms by providing further information according to the algal dynamics for inland water management with remote sensing images.

Distinguishing and Matching-Aware Unsupervised Point Cloud Completion

Real-scanned point clouds are often incomplete due to occlusion, light reflection and limitations of sensor resolution, which impedes the related progress of downstream tasks, e.g., shape classification and object detection. Although there has been impressive research progress on the point cloud completion topic, they rely on the premise of extensive paired training data. However, collecting complete point clouds in some specified scenarios is labor-intensive and even impractical. To mitigate this problem, we propose DMNet, a distinguishing and matching-aware unsupervised point cloud completion network. Our work belongs to the group of unsupervised completion methods but goes beyond previous studies. Firstly, we propose a distinguishing-aware feature extractor to learn discriminable semantic information for different instances, simultaneously enhancing the robust invariant representation under noise disturbances. Secondly, we design a hierarchy-aware hyperbolic decoder to recover the complete geometry of point clouds, which not only can capture the implicit hierarchical relationships in data but also has an explicit extended nature. Finally, we develop a matching-aware refiner to eliminate noise points via aligning the topology structure of the input and predicted partial point clouds. Extensive experiments on MVP, Completion3D and KITTI datasets prove the effectiveness of our method, which performs favorably over state-of-the-art methods both quantitatively and qualitatively.

UAV hyperspectral remote sensor images for mango plant disease and pest identification using MD-FCM and XCS-RBFNN.

To diminish disease transmission together with promoting effective management techniques, it is crucial to monitor plant health and detect pathogens earlier. The initial part in reducing losses sourced from plant diseases is to make an accurate and earlier identification. Thus, the usage of unmanned aerial vehicle (UAV) hyperspectral imaging (HSI) sensors for surveying and assessing crops, orchards, and forests has rapidly elevated over the last decade, particularly for the stress management like water, diseases, nutrition deficits, and pests. Using Minkowski Distance-based Fuzzy C Means (MD-FCM) clustering and Xavier initialization-adapted Cosine Similarity-induced Radial Bias Function Neural Network (XCS-RBFNN) techniques, a UAV HS imaging remote sensor for Spatial and Temporal Resolution (STR) of mango plant disease and pest identification is proposed in this scheme. Collecting the input UAV source (image or video) is eventuated initially along with the Region of Interest (ROI) calculated which is followed by preprocessing. Leaf segmentation is eventuated using Logistic U-net after preprocessing. Next, MD-FCM performs clustering to cluster the diseased leaves and pests individually. The disease and pest characteristics are then retrieved separately and classified further. The requisite features are then chosen from the retrieved features utilizing the Levy Flight Distribution-produced Butterfly Optimization Algorithm (LFD-BOA). Finally, the XCS-RBFNN classifier is utilized to categorize the various diseases together with pests found in the UAV input source using the chosen features. The proposed framework's experimental findings are then compared to some prevailing schemes, with the results revealing that the proposed work outperforms other benchmark techniques.

A Theoretical Approach for Detecting the Chikungunya Virus Based on 1D Photonic Crystals

In the current study, it is aimed at using defective 1D photonic crystals (PCs) to detect the chikungunya virus in various healthy and diseased blood samples composed of plasma, platelets, red blood cells, and uric acid. The proposed PC structure has 14 periods and consists of repeating SiC and SiO2 layers with a central cavity layer. When blood samples are injected into the cavity layer, the transmittance spectrum is examined theoretically by using a transfer matrix approach to determine how the wavelength of the defect mode changes. The cavity layer is 540 and 648 nm thick, and the work is carried out at different angles of incidence. The performance of the sensor is quantified by computing the sensitivity, figure of merit, quality factor, and limit of detection values of the sensor for various blood samples. The maximum sensitivity is 1205.5 nm RIU−1 and detection limit of the order is 10−6 in this proposed work. A lower value of sensor resolution of 0.01218 is also achieved. Such a high‐performance sensor is suitable for biosensing applications with better sensing capabilities.

Super-Resolution Reconstruction of Remote Sensing Data Based on Multiple Satellite Sources for Forest Fire Smoke Segmentation

Forest fires are one of the most devastating natural disasters, and technologies based on remote sensing satellite data for fire prevention and control have developed rapidly in recent years. Early forest fire smoke in remote sensing images, on the other hand, is thin and tiny in area, making it difficult to detect. Satellites with high spatial resolution sensors can collect high-resolution photographs of smoke, however the impact of the satellite’s repeat access time to the same area means that forest fire smoke cannot be detected in time. Because of their low spatial resolution, photos taken by satellites with shorter return durations cannot capture small regions of smoke. This paper presents an early smoke detection method for forest fires that combines a super-resolution reconstruction network and a smoke segmentation network to address these issues. First, a high-resolution remote sensing multispectral picture dataset of forest fire smoke was created, which included diverse years, seasons, areas, and land coverings. The rebuilt high-resolution images were then obtained using a super-resolution reconstruction network. To eliminate data redundancy and enhance recognition accuracy, it was determined experimentally that the M11 band (2225–2275 nm) is more sensitive to perform smoke segmentation in VIIRS images. Furthermore, it has been demonstrated experimentally that improving the accuracy of reconstructed images is more effective than improving perceptual quality for smoke recognition. The final results of the super-resolution image segmentation experiment conducted in this paper show that the smoke segmentation results have a similarity coefficient of 0.742 to the segmentation results obtained using high-resolution satellite images, indicating that our method can effectively segment smoke pixels in low-resolution remote sensing images and provide early warning of forest fires.

Flight Experiments and Numerical Simulations for Investigating Multicopter Flow Field and Structure Deformation

The flow field induced by multirotor drones is of high interest for atmospheric research, as it locally influences the atmosphere and therefore may have an impact on the sensors installed for atmospheric measurements. Further, on-board vibrations can cause significant interference with the measurement equipment. To investigate the near flow field, an approach combining measurements of pressure and temperature distribution in-flight and in a laboratory setup together with numerical simulations was applied. Existing high-frequency measurement equipment was piggybacked during the initial flight tests with a newly developed 25 kg quadcopter system in a low-cost early-stage-error approach to obtain initial data and experience. During the flights, high resolution sensors for measuring pressure, temperature, acceleration, and deformation were applied with different setups at different locations below one of the rotor planes, respectively, at one rotor arm, to determine the multicopter’s influence on pressure and temperature measurements, to investigate rotor arm deformations, and to obtain data to compare with numerical simulations of this rotor setup. An external Schlieren-type measurement technique was tested to visualise the rotor vortices. The applied measurement techniques proved to be suitable for acquiring the state of the rotor-induced flow, but with some limitations. The comparison of measurements and simulations showed basic agreement and allowed for the identification of necessary adaptations for subsequent studies. The interaction of the rotor wakes with the rotor arms could be identified as the main source of the measured structural vibrations. The need for necessary improvements in the measurement setup, flight operation, and simulation setup is presented in detail.

An Open Image Resizing Framework for Remote Sensing Applications and Beyond

Image resizing (IR) has a crucial role in remote sensing (RS), since an image’s level of detail depends on the spatial resolution of the acquisition sensor; its design limitations; and other factors such as (a) the weather conditions, (b) the lighting, and (c) the distance between the satellite platform and the ground targets. In this paper, we assessed some recent IR methods for RS applications (RSAs) by proposing a useful open framework to study, develop, and compare them. The proposed framework could manage any kind of color image and was instantiated as a Matlab package made freely available on Github. Here, we employed it to perform extensive experiments across multiple public RS image datasets and two new datasets included in the framework to evaluate, qualitatively and quantitatively, the performance of each method in terms of image quality and statistical measures.

Dual discriminators generative adversarial networks for unsupervised infrared super-resolution

The breakthrough of sensor resolution has always been focused on the computer vision area, which is an urgent need when infrared sensor technology progresses slowly. We propose a novel unsupervised infrared super-resolution (UISR) method based on the photoelectric characteristics of infrared images. The UISR, without high-resolution ground truth, establishes a fully unpaired generative adversarial framework to enrich the image details and improve the visual perception of human eyes. In the process of SR, UISR corrects the abnormal pixel values, blurred edges, and mosaic effect by maintaining the image style uniformity and mining the texture distribution characteristics in the image domain. Specifically, the dual discriminator module is designed to enhance both global structures and local textures. The style discriminator captures the natural structure characteristics of large image patches to control the authenticity. In contrast, the texture discriminator captures the texture characteristics of small image patches to improve the detail generation performance. Besides, the content constraint module is designed to maintain the basic content of the image. Experiments show that, compared to the supervised algorithm, the proposed UISR presents more real SR infrared images and is simpler to generalize in higher scales beyond sensor resolution.

A MODIS–Landsat cloud-based spatiotemporal downscaling algorithm to estimate land surface temperature

ABSTRACT Land Surface Temperature (LST) is a key variable in the energy and water balance between the surface and the atmosphere. LST is typically retrieved from remote sensing data because of the lack of sufficient flux towers for meteorological data collection, which makes local availability of LST scarce. Remote sensing can provide data for large areas, but the spatial resolution of the data may be coarser than the temporal resolution (e.g. a daily revisit interval for satellite-based Moderate-Resolution Imaging Spectroradiometer (MODIS) sensors), or conversely, the spatial resolution may be coarser than the temporal resolution (e.g. Landsat satellite images obtained every 16 days). This tradeoff between spatial and temporal resolutions has motivated researchers to develop methods and algorithms to create image collections with both high spatial and temporal resolutions. In this study, we used a cloud-based downscaling algorithm to create LST images with high temporal and spatial resolution for the southern region of Brazil using Google Earth Engine (GEE). A comparison of the downscaling estimates with thermal Landsat images showed that 65% of the root mean squared errors (RMSE) were lower than 2 K, and 77% of the correlation coefficients (R) were greater than 0.70, while 84% of the bias values were between −2 and 2 K. The downscaling estimates were also validated using in situ measurements. Overall, a comparison between in situ measurements and different LST retrieval methods differed slightly in accuracy, with average RMSE values between 1.55 and 4.32 K, bias between −1.07 and 3.96 K, and correlation coefficients of nearly 0.90. These results demonstrate that cloud computing can be used to retrieve LST with high spatiotemporal resolution.

Extended Object Tracking Using Random Matrix With Extension-Dependent Measurement Numbers

For extended object tracking (EOT), a fluctuating number of measurements are generated by a sensor at a time instant. In practice, the measurement number depends on the object extension, sensor resolution, and sensor-to-object geometry. Given the sensor resolution, the number thus contains information on the object state and extension. This paper proposes a random-matrix approach to EOT utilizing this information to improve the performance of state and extension estimation. First, a Gamma-alike distribution of the measurement number is proposed to model the dependency of the number on sensor resolution and the object state and extension. This model also fits the random-matrix framework. Second, a Bayesian approach to jointly estimating the state and extension based on an extension-dependent number of measurements. Facilitated by the form of the distribution, the derived approach has an analytical form and it can naturally reduce to an EOT approach without directly using measurement numbers. The proposed number model can also be incorporated with different random-matrix approaches. The effectiveness of the proposed approach is demonstrated by evaluation results using one simulation and two real-data experimental scenarios compared with existing random-matrix algorithms.

UnTDIP: Unsupervised neural network for DEM super-resolution integrating terrain knowledge and deep prior

Digital Elevation Models (DEMs) are essential for comprehending the three-dimensional (3D) structure of the Earth’s surface. When the resolution of airborne or satellite sensors fail to meet the requirements for high-precision terrain observation, super-resolution methods offer an alternative solution, which infers the high-resolution DEMs from coarse-resolution DEMs. This study aims to create an unsupervised DEM super-resolution method that utilizes untrained neural networks, integrating both deep prior from network structure and terrain knowledge, namely UnTDIP. Specifically, the untrained neural network iteratively processes a single DEM, and the deep prior from network structure learns low-order statistical information from the DEM that is then applied to the super-resolution task. Furthermore, the terrain knowledge for topographic consistency is integrated into the iterative process and synergies with the deep prior to reconstructing the DEM with the correct 3D structure. Most neural network-based DEM super-resolution methods are data-driven, while the proposed method does not require any training datasets or pre-trained network parameters, making it broadly applicable. The UnTDIP method was evaluated in three study areas with different spatial resolutions of 5 m, 10 m, and 30 m. The method performs well in multiple scale factors and spatial resolutions, even surpassing the fully supervised DEM super-resolution method in some evaluation metrics. Moreover, the integration of terrain knowledge reduces the Mean Absolute Error (MAE) by 6.08 % to 34.65 %. Overall, the proposed method is well-suited for DEM data and can be adapted for super-resolution tasks for other geospatial data with the integration of domain knowledge.