Interpolation Process Research Articles

Agricultural Drought Monitoring by MODIS Potential Evapotranspiration Remote Sensing Data Application

The current Polish Agricultural Drought Monitoring System (ADMS) adopted Climatic Water Balance (CWB) as the main indicator of crop losses caused by drought conditions. All meteorological data needed for CWB assessment are provided by the ground meteorological stations network. In 2018, the network consisted of 665 stations, among which in only 58 stations full weather parameters were registered. Therefore, only these stations offered a possibility to estimate the exact values of potential evapotranspiration, which is a component of the CWB algorithm. This limitation affects the quality of CWB raster maps, interpolated on the basis of the meteorological stations network for the entire country. However, the interpolation process itself may introduce errors; therefore, the adaptation of satellite data (that are spatially continuous) should be taken into account, even if the lack of data due to cloudiness remains a serious problem. In this paper, we involved the remote sensing data from MODIS instrument and considered the ability to integrate those data with values determined by using ground measurements. The paper presents results of comparisons for the CWB index assessed using ground station data and those obtained from potential evapotranspiration as the product from Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing instrument. The comparisons of results were performed for specific points (locations of ground stations) and were expressed by differences in means values. Analysis of Pearson’s correlation coefficient (r), Mann–Kendal trend test (Z-index), mean absolute error (MAE) and root mean square error (RMSE) for ten years’ series were evaluated and are presented. In addition, the basic spatial interpretation of results has been proposed. The correlation test revealed the r coefficient in the range from 0.06 to 0.68. The results show good trend agreement in time between two types of CWB with constantly higher values of this index, which is estimated using ground measurement data. In results for 34 (from 43 analyzed) stations the Mann–Kendal test provide the consistent trend, and only nine trends were inconsistent. Analyses revealed that the disagreement between the two considered indices (determined in different ways) increased significantly in the warmer period with a significant break point between R7 and R8 that falls at the end of May for each examined year. The value of MAE varied from 80 mm to 135 mm.

ALL-RUSSIAN PHYSICAL EDUCATION AND SPORTS PROGRAM “READY FOR LABOR AND DEFENSE” (GTO)

Aim. The paper aims to substantiate the improvement of the All-Russian physical education and sports program “Ready for labor and defense” (GTO) for disabled people and persons with health limitations (PHL). Materials and methods. The research was carried out through the analysis of documentary materials and legal acts, questionnaires and expert assessments, as well as using testing, approximation, extrapolation, interpolation, and statistical processing of empirical data. Results. The concept of GTO for PHL was developed, which is fundamentally different from the general program for healthy citizens and is focused on basic physical qualities of PHL and their terminal values through healthy lifestyle. The implementation of the program after its approval by the Ministry of Sport of the Russian Federation in 2019 is analyzed. Based on the concept of the GTO program for PHL and its first results, prospects for program improvement and implementation are justified. Conclusion. The implementation of the GTO program for PHL will allow for the comprehensive rehabilitation and habilitation of disabled people, as well as children and persons with health limitations. The program itself will increase their motivation for regular (systematic) exercises in adaptive physical education and adaptive sports and contribute to the achievement of a better life quality.

Inter-comparison of several soil moisture downscaling methods over the Qinghai-Tibet Plateau, China

Microwave remote sensing is able to retrieve soil moisture (SM) at an adequate level of accuracy. However, these microwave remotely sensed SM products usually have a spatial resolution of tens of kilometers which cannot satisfy the requirements of fine to medium scale applications such as agricultural irrigation and local water resource management. Several SM downscaling methods have been proposed to solve this mismatch by downscaling the coarse-scale SM to fine-scale (several kilometers or hundreds of meters). Although studies have been conducted over different climatic zones and from different data sets with good results, there is still a lack of a comprehensive comparison and evaluation between them to guide the production of high-resolution and high-accuracy SM data. Therefore, in this study we compared several SM downscaling methods (from 0.25° to 0.01°) based on polynormal fitting, physical model, machine learning and geostatistics over the Qinghai-Tibet plateau where there is a wide range of climate conditions from four aspects, that is, comparison with the original microwave product, comparison with in situ measurements, inter-comparison based on three-cornered hat (TCH) method, and a spatial feasibility analysis. The comparison results show that the method based on a physical model, in this case the Disaggregation based on Physical And Theoretical scale Change (DisPATCh) method, has the highest ability on preserving the coarse-scale feature of original microwave SM product, while to some extent, this ability could be a disadvantage for improving the accuracy of the downscaling results. In addition, soil evaporation efficiency (SEE) alone is not sufficient to represent SM spatial patterns over complex land surface. Geostatistics based area-to-area regression Kriging (ATARK) introduces the highest uncertainty caused by the overcorrection during the residual interpolation process while this process can also improve correlation (R) and correct the bias as well as provide more feasible spatial patterns and details. Two machine learning methods, the random forest (RF) and Gaussian process regression (GPR) show high stability on all comparison results but provide smoother spatial patterns. The multivariate statistical regression (MSR) method performs worst due to the fact that its simple linear regression model could not meet the requirement of SM fitting on complicated land surface. Moreover, all five downscaling methods show a declining accuracy after downscaling. This phenomenon may be caused by the spatial mismatch on fine-scale. In addition, this could also be caused by the tendency that downscaled results will usually provide more spatial details from downscaling predictors, while they cannot capture the temporal changes of the microwave SM product well. In general, this phenomenon tends to be more significant over heterogeneous land surface. All in all, five widely used soil moisture downscaling methods were compared based on a comprehensive comparison scheme to add to the body of knowledge in applicability of downcaling methods under different weather conditions.

Complex interpolation of families of Orlicz sequence spaces

We study the complex interpolation and derivation process induced by a family of Orlicz sequence spaces. We present a concrete example of an interpolation family of three spaces inducing a centralizer that cannot be obtained from complex interpolation of two spaces.

Seamless low-temperature thermochronological modeling in Andino 3D, towards integrated structural and thermal simulations

We present the development of thermochronological tools for Andino 3D® software, that integrates Fetkin (Finite Element Temperature Kinematics). These tools allow the user to work on both the structural and the thermochronological model at the same time, providing a user-friendly environment that overcomes the need to work with different programs. Thermochronological and structural models can be checked and eventually corrected in a visual and intuitive form by following a 4-step workflow. The first step of such workflow is to define the thermochronological computing grid, checking in real time, if the resolution and coverage are satisfactory. After that, the interpolation process can be done, whereby velocity vectors for all nodes in beds and faults are calculated for all interpolated times. The third step of the workflow consists of filling thermal properties and velocities for all grid cells. The final step is the calculation of the thermal state at each time in the reconstruction. Boundary conditions (basal temperature, basal heat flow, surface temperature and altitude gradient) are defined by mouse picking as constant, spatially varying, time varying or spatially and time varying. To check the feasibility of a structural model, thermochronological samples can be defined at desired positions to predict time-temperature variations. Simulated fission track ages, mean track lengths and age standard deviations can be calculated for different minerals (apatite and zircons). Also, cooling ages and %Ro can be simulated for (U–Th-Sm)/He and vitrinite systems, respectively. The Carohuaicho structure in the southern Bolivia sub-Andean Ranges is presented as a case of study to demonstrate these tools. Andino 3D® allowed us to successfully simulate the t-T paths of four samples where (U–Th-Sm)/He measurements were available. The different models performed permitted us to conclude that a low geothermal gradient was likely to be present during the last 7 Ma of Andean deformation in the study region.

A Spatial Interpolation of Meteorological Parameters considering Geographic Semantics

Spatial interpolation of meteorological parameters, closely related to the earth surface, plays important roles in climatological study. However, most of traditional spatial interpolation methods ignore the geographic semantics of interpolation sample points in practical application. This paper attempts to propose an improved inverse-distance weighting interpolation algorithm considering geographic semantics (S-IDW), which adds geographic semantic similarity to the traditional IDW formula and adjusts weight coefficient. In the interpolation process, the geographic semantic differences between sample points and estimation points are considered comprehensively. In this study, 3 groups of land surface temperature data from 2 different areas were selected for experiments, and several commonly used spatial interpolation methods were compared. Experimental results indicated that S-IDW outperformed IDW and several existing spatial interpolation methods, but there were also some abnormal value and interpolation outliers. This method provides a new insight toward the estimation accuracy, data missing, and error correction of spatial attributes related to meteorological parameters.

Application of Nonhydraulic Delineation Method of Flood Hazard Areas Using LiDAR-Based Data

Fluvial dynamics are an important aspect of land-use planning as well as ecosystem conservation. Lack of floodplain and flood inundation maps can cause severe implication on land-use planning and development as well as in disaster management. However, flood hazard delineation traditionally involves hydrologic models and uses hydraulic data or historical flooding frequency. This entails intensive data gathering, which leads to extensive amount of cost, time, and complex models, while typically only covers a small portion of the landscape. Therefore, alternative approaches had to be explored. This study explores an alternative approach in delineating flood hazard areas through a straightforward interpolation process while using high-resolution LiDAR-based datasets. The objectives of this study are: (1) to delineate flood hazard areas through a straightforward, nonhydraulic, and interpolation procedure using high-resolution (LiDAR-based) datasets and (2) to determine whether using high-resolution data, coupled with a straightforward interpolation procedure, will yield reliable potential flood hazard maps. Results showed that a straightforward interpolation method using LiDAR-based data produces a reliable potential flood zone map. The resulting map can be used as supplementary information for rapid analysis of the topography which could have implications in area development planning and ecological management and best practices.

A novel reduced-order guidance and control scheme for hypersonic gliding vehicles

A novel analytical model between roll, pitch rates and components of acceleration of the hypersonic vehicle is established, and a reduced-order gliding guidance and control (G&C) design approach for the hypersonic vehicle is proposed in this paper. The six-degree-of-freedom (6DOF) dynamical and kinematic equations of the hypersonic vehicle in the gliding phase are described. An analytical model suitable for the bank to turn (BTT) control strategy is derived by connecting body rates with components of acceleration in the ballistic coordinate system. A novel reduced-order guidance and control design model with respect to quasi-equilibrium gliding flight is deduced and converted into the strict feedback form. The three channel body rates are treated as pseudo-inputs for tracking errors of flight path angle and heading angle of the hypersonic vehicle. Then, the commanded control surface fin deflections of the hypersonic vehicle are obtained by controlling tracking errors of the flight path angle, heading angle, and the body rates by adopting the block dynamic surface control method. In this paper, the aerodynamic coefficient interpolation process for obtaining the commanded angle of attack and bank angle based on expectant guidance overload is avoided. Meanwhile, the control loop of tracking Euler angles for getting the desired body rates is also elided. Finally, effectiveness and robustness of the novel G&C design method are verified and investigated. Moreover, the feasibility of entering quasi-equilibrium gliding phase only depending on the aerodynamic control mechanism is discussed.

Enhanced wide-area multi-GNSS RTK and rapid static positioning in the presence of ionospheric disturbances

Ionospheric disturbances are the phenomena which adversely affect the performance of precise positioning. This holds true even for multi-constellation relative positioning supported with network-derived ionospheric corrections. In such scenario the unfavorable effect is caused by a poor accuracy of corrections, which, in turn, is driven by the deterioration of the spatial interpolation process. The positioning becomes even more challenging in a wide-area scenario with baselines over 100 km. In this paper, we assess the methodology which aims at reliable and accurate wide-area RTK and rapid static positioning in the presence of severe ionospheric conditions. The approach takes advantage of multi-constellation network ionospheric corrections and an algorithm which allows the elimination of the temporal variations of the ionospheric delay. The experimental evaluation was performed on the basis of multi-station RTK and static positioning using GPS, BDS and Galileo data collected at high latitudes during the ionospheric storm on August 25–26, 2018. The results confirmed the deterioration of the accuracy of the network ionospheric corrections and consequently a decline in the positioning performance with routine models such as ionosphere-float and ionosphere-weighted. On the other hand, the results obtained with the application of the developed methodology demonstrated a very distinctive improvement in the ambiguity resolution domain and thus proved the advantage over benchmark models. In this case, the developed methodology allowed up to 20% enhancement of the ambiguity success rate with respect to benchmark strategies.

RETRACTED ARTICLE: A novel patch selection technique in ANN B-Spline Bayesian hyperprior interpolation VLSI architecture using fuzzy logic for highspeed satellite image processing

During recent days various research are going on Satellite image processing including remote sensing, agriculture, disaster management and various other fields. Denoising and interpolation are two major tasks in Image processing. These tasks assist screening and interpreting many scientific images such as medical images and satellite images. The proposed work named as “fuzzy logic-based Patch Selection in ANN B-Spline Bayesian hyperprior interpolation VLSI architecture” (FLABBHI) is contrived in the indent of designing a new VLSI architecture for faster satellite image denoising and interpolation. Non-native image sampling process for denoising is performed using fuzzy logic based patch selection and artificial neural network-based B-Spline Bayesian hyperprior interpolation (ABBHI) procedure is used for interpolation process. Fuzzy logic is used to improve the execution speed in the process of selecting patch images from a collection of received images to construct the image more similar toground-truth image. For experimentation, FLABBHI is designed for the target hardware Kintex, used to construct and evaluate the proposed method. Xilinx ISE is used to measure the performance metrics of the target FPGA. The interpolation is performed by ABBHI method to improve the quality of the constructed image during the interpolation process. A real-time mode is included in the proposed FLABBHI to buffer and process incoming images from a satellite lively. This real-time mode is equipped to run with the incremental rendering process which is used to get a quick scanning in the beginning and more accurate reconstructed image over the time.

AN ADAPTIVE IMAGE SCALING ALGORITHM BASED ON CONTINUOUS FRACTION INTERPOLATION AND MULTI-RESOLUTION HIERARCHY PROCESSING

Traditional interpolation algorithms often blur the edges of the target image due to low-pass filtering effects, making it difficult to obtain satisfactory visual effects. Especially when the reduction ratio becomes small, the phenomenon of jagged edges and partial information loss will occur. In order to obtain better image scaling quality, an adaptive image scaling algorithm based on continuous fraction interpolation and multi-resolution hierarchical processing is proposed. In order to overcome the noise problem of the original image, this paper first performs wavelet decomposition on the original image to obtain multiple images with different resolutions. Secondly, in order to eliminate the influence of local area variance on the overall image, weighted average is performed on images of different resolutions. Then, in order to overcome the blurring effect of the weighted average image, by calculating the variance of the three groups of pixels around the target pixel, selecting a group of pixels with the smallest variance and using the Salzer continuous fraction interpolation equation to obtain the gray value of the target pixel. Finally, the multiple corrected images are stitched together into a scaled image. The algorithm in this paper achieves a high-order smooth transition between pixels in the same feature area, and can also adaptively modify the pixels of the image. The experimental results show that the edge of the target image obtained by the algorithm in this paper is clear, and the algorithm complexity is low, which is convenient for hardware implementation and can realize real-time image scaling.

Spatiotemporal Precipitation Estimation from Rain Gauges and Meteorological Radar Using Geostatistics

Automatic interpolation of precipitation maps combining rain gauge and radar data has been done in the past but considering only the data collected at a given time interval. Since radar and rain gauge data are collected at short intervals, a natural extension of previous works is to account for temporal correlations and to include time into the interpolation process. In this work, rainfall is interpolated using data from the current time interval and the previous one. Interpolation is carried out using kriging with external drift, in which the radar rainfall estimate is the drift, and the mean precipitation is set to zero at the locations where the radar estimate is zero. The rainfall covariance is modeled as non-stationary in time, and the space system of reference moves with the storm. This movement serves to maximize the collocated correlation between consecutive time intervals. The proposed approach is analyzed for four episodes that took place in Catalonia (Spain). It is compared with three other approaches: (i) radar estimation, (ii) kriging with external drift using only the data from the same time interval, and (iii) kriging with external drift using data from two consecutive time intervals but not accounting for the displacement of the storm. The comparisons are performed using cross-validation. In all four episodes, the proposed approach outperforms the other three approaches. It is important to account for temporal correlation and use a Lagrangian system of coordinates that tracks the rainfall movement.

Impact of interpolation techniques on the accuracy of large-scale digital elevation model

Abstract There is no doubt that the tremendous development of information technology was one of the driving factors behind the great growth of surveying and geodesy science. This has spawned modern geospatial techniques for data capturing, acquisition, and visualization tools. Digital elevation model (DEM) is the 3D depiction of continuous elevation data over the Earth’s surface that is produced through many procedures such as remote sensing, photogrammetry, and land surveying. DEMs are essential for various surveying and civil engineering applications to generate topographic maps for construction projects at a scale that varies from 1:500 to 1:2,000. GIS offers a powerful tool to create a DEM with high resolution from accurate land survey measurements using interpolation methods. The aim of this research is to investigate the impact of estimation techniques on generating a reliable and accurate DEM suitable for large-scale mapping. As a part of this study, the deterministic interpolation algorithms such as ANUDEM (Topo to Raster), inverse distance weighted (IDW), and triangulated irregular network (TIN) were tested using the ArcGIS desktop for elevation data obtained from real total station readings, with different landforms to show the effect of terrain roughness, data density, and interpolation process on DEM accuracy. Furthermore, comparison and validation of each interpolator were carried out through the cross-validation method and numerous graphical representations of the DEM. Finally, the results of the investigations showed that ANUDEM and TIN models are similar and significantly better than those attained from IDW.

Pseudo-3D seismic construction using tau-p transform interpolation

Three dimensional (3D) seismic imaging is very important in hydrocarbon exploration. However, 3D seismic data acquisition costs more expensive than 2D seismic data acquisition. Therefore, the explorations are mostly done with some 2D seismic lines and requiring some interpolation method to construct the 3D seismic model called Pseudo-3D Seismic. In other word, the 3D model depends much on the interpolation process for a more reliable result. There have been many interpolation methods used to construct the 3D seismic model. Tau-P Transform is one of the methods that can be used to construct a 3D seismic model. It has been used to reconstruct seismic data and fill the near-offset gap in marine seismic data for decades. In this research, the method is done by having the crossline of some 2D seismic lines and performing the transformation from t-x domain to the tau-p domain. After the transformation, by inverse-transformation from tau-p to t-x domain, we might perform interpolation along the line to construct more traces with regular and near spacing. This method can be performed to all crosslines and construct Pseudo-3D Seismic. The results show constructed crosslines with well-lineated layers. The crosslines show developed layers are like those of the inline implying that the interpolation successfully construct the layers based on the inline.

Image Demosaicing Based on Generative Adversarial Network

Digital cameras with a single sensor use a color filter array (CFA) that captures only one color component in each pixel. Therefore, noise and artifacts will be generated when reconstructing the color image, which reduces the resolution of the image. In this paper, we proposed an image demosaicing method based on generative adversarial network (GAN) to obtain high-quality color images. The proposed network does not need any initial interpolation process in the data preparation phase, which can greatly reduce the computational complexity. The generator of the GAN is designed using the U-net to directly generate the demosaicing images. The dense residual network is used for the discriminator to improve the discriminant ability of the network. We compared the proposed method with several interpolation-based algorithms and the DnCNN. Results from the comparative experiments proved that the proposed method can more effectively eliminate the image artifacts and can better recover the color image.

How does co-registration affect geomorphic change estimates in multi-temporal surveys?

ABSTRACT High-Resolution Topography (HRT) data sets are becoming increasingly available, improving our ability and opportunities to monitor geomorphic changes through multi-temporal Digital Terrain Models (DTMs). The use of repeated topographic surveys enables inferring the sediment dynamics of hazardous geomorphic processes such as floods, debris flows, and landslides, and allows us to derive important information on the risks often associated with these processes. The topographic surveying platforms, georeferencing systems, and processing tools have seen important developments in the last two decades, in particular Light Detection And Ranging (LiDAR) technology used in Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS). Moreover, HRT data, produced through these techniques, changed a lot in terms of point cloud density, accuracy and precision over time. Therefore, old “legacy” data sets and recent surveys can often show comparison problems, especially when multi-temporal data are not homogeneous in terms of quality and uncertainties. In this context, data co-registration should be used to guarantee the coherence among multi-temporal surveys, minimizing, on stable areas, the distance between corresponding points acquired at different epochs. Although several studies highlight that this process is fundamental to properly compare multi-temporal DTMs, it is often not addressed in LiDAR post-processing workflows. In this paper we focus on the alignment of multi-temporal surveys in a topographically complex and rugged environment as the Moscardo debris-flow catchment (Eastern Italian Alps), testing various co-registration methods to align multi-temporal ALS point clouds (i.e. years 2003, 2009 and 2013) and the derived DTMs. In particular, we tested the pairwise registration with manual correspondences, the Iterative Closest Point (ICP) algorithm and a mathematical model that allows aligning simultaneously a generic number of point clouds, the so-called Generalized Procrustes Analysis (GPA), also in its GPA-ICP variant. Then, to correct the possible small inaccuracies generated from the gridding interpolation process, a custom-developed DTM co-registration tool (GRD-CoReg) was used to align gridded data. Both alignment phases (i.e. at point cloud and DTM level) proved to be fundamental and allowed us to obtain proper and reliable DTMs of Difference (DoDs), useful to quantify the debris mobilized and to detect the spatial and temporal patterns of catchment-scale erosion and deposition. The consistency of DoDs data was verified through the comparison between the erosion estimate of DoDs and the volumes of debris-flow events measured by the monitoring station close to the Moscardo torrent catchment outlet. The GPA-ICP algorithm followed by the GRD-CoReg tool proved to be the most effective solution for improving DoDs results with a decrease of systematic trend due to vertical and horizontal uncertainties between surveys, especially at steep slopes. The net volume difference (i.e. the sediment output from the catchment) of the 2003–2013 period changed from 3,237,896 m3 to 135,902 m3 in DoDs obtained from not co-registered and co-registered DTMs. The volume of debris flows measured at the catchment outlet during the same time interval amounts to 169,660 m3. The comparison with debris-flow volume measures at the monitoring station shows, therefore, that the DTMs obtained from the co-registration processes generate more reliable DoDs than those obtained from the raw DTMs (without the alignment).

A Vector Field Texture Generation Method without Convolution Calculation

In the LIC algorithm process, symmetrical streamline tracing is used to symmetrically convolve the original values of all the primitive values that pass by to obtain the resulting texture. In this process, streamline tracking and convolution consume a lot of computing resources. To generate more expressive textures for vector fields with less time consumption, a novel method named random increment streamline (RIS) is put forward, which can generate streamline textures without convolution calculations. First, the mesh unit filling preprocessing (MUFP) method is presented to transform an undressed irregular grid into a special kind of regular grid named a “texture pixel”, and the point location and interpolation processes of all sampling points in the texture pixels are calculated before streamline tracking. Second, the random increment streamline method is used to generate line integral convolution style textures without any convolution calculations, thus greatly reducing the algorithm’s time consumption. Third, the vector directions at each point in the static vector field are clearly expressed using the periodic cyclic animation method. Finally, several simplifications of the RIS algorithm are discussed, which help to achieve a better visual effect with faster speed. The programming results show that the method is faster and more applicable than the traditional LIC method and provides clearer expression of the vector field.

Soil fertility status of available phosphorus in soil through soil fertility mapping using GPS and GIS techniques of dharmaur micro-watershed Jagdalpur block, Bastar District of (C.G.) State

The present research on the soil fertility status of available phosphorus in soil by soil fertility mapping using GPS and GIS techniques of the Dharmaur microwatershed Jagdalpur block, Bastar district of C.G. The goal of the study was to quantify the soil fertility status of available phorphorus in Dharmaur micro-watershed soils through soil fertility mapping using GPS and GIS techniques. Soil fertility maps have been modified using the Geostatistical Interpolation Process Kriging technique. The soil fertility analysis of the study area will be carried out on a scale of 1:10000. Grid point based soil samples (0-15 cm) will be collected, dried, grounded and processed for part of the villages of Kumhrawand and Tekameta and surrounded by the village of Kumhrawand to the east, the village of Tekameta to the west, the river Indravati to the north and the villages of Bhadisgaon & Biringpal to the south at a distance of 100 metres.

Inverse distance weighting method optimization in the process of digital terrain model creation based on data collected from a multibeam echosounder

This paper presents the optimization of the inverse distance weighting method (IDW) in the process of creating a digital terrain model (DTM) of the seabed based on bathymetric data collected using a multibeam echosounder (MBES). There are many different methods for processing irregular measurement data into a grid-based DTM, and the most popular of these methods are inverse distance weighting (IDW), nearest neighbour (NN), moving average (MA) and kriging (K). Kriging is often considered one of the best methods in interpolation of heterogeneous spatial data, but its use is burdened by a significantly long calculation time. In contrast, the MA method is the fastest, but the calculated models are less accurate. Between them is the IDW method, which gives satisfactory accuracy with a reasonable calculation time. In this study, the author optimized the IDW method used in the process of creating a DTM seabed based on measurement points from MBES. The goal of this optimization was to significantly accelerate the calculations, with a possible additional increase in the accuracy of the created model. Several variants of IDW methods were analysed (dependent on the search radius, number of points in the interpolation, power of the interpolation and applied smoothing method). Finally, the author proposed an optimization of the IDW method, which uses a new technique of choosing the nearest points during the interpolation process (named the growing radius). The experiments presented in the paper and the results obtained show the true potential of the IDW optimized method in the case of DTM estimation.

Image Interpolation via Gradient Correlation-Based Edge Direction Estimation

This paper introduces an image interpolation method that provides performance superior to that of the state-of-the-art algorithms. The simple linear method, if used for interpolation, provides interpolation at the cost of blurring, jagging, and other artifacts; however, applying complex methods provides better interpolation results, but sometimes they fail to preserve some specific edge patterns or results in oversmoothing of the edges due to postprocessing of the initial interpolation process. The proposed method uses a new gradient-based approach that makes an intelligent decision based on the edge direction using the edge map and gradient map of an image and interpolates unknown pixels in the predicted direction using known intensity pixels. The input image is subjected to the efficient hysteresis thresholding-based edge map calculation, followed by interpolation of low-resolution edge map to obtain a high-resolution edge map. Edge map interpolation is followed by classification of unknown pixels into obvious edges, uniform regions, and transitional edges using the decision support system. Coefficient-based interpolation that involves gradient coefficient and distance coefficient is applied to obvious edge pixels in the high-resolution image, whereas transitional edges in the neighborhood of an obvious edge are interpolated in the same direction to provide uniform interpolation. Simple line averaging is applied to pixels that are not detected as an edge to decrease the complexity of the proposed method. Applying line averaging to smooth pixels helps to control the complexity of the algorithm, whereas applying gradient-based interpolation preserves edges and hence results in better performance at reasonable complexity.