Geometric Transformation Model Research Articles

Selective weighted least square and piecewise bilinear transformation for accurate satellite DSM generation

One of the main products of multi-view stereo (MVS) high-resolution satellite (HRS) images in photogrammetry and remote sensing is digital surface model (DSM). Producing DSMs from MVS HRS images still faces serious challenges due to various reasons such as complexity of imaging geometry and exterior orientation model in HRS, as well as large dimensions and various geometric and illumination variations. The main motivation for conducting this research is to provide a novel and efficient method that enhances the accuracy and completeness of extracting DSM from HRS images compared to existing recent methods. The proposed method called Sat-DSM, consists of five main stages. Initially, a very dense set of tie-points is extracted from the images using a tile-based matching method, phase congruency-based feature detectors and descriptors, and a local geometric consistency correspondence method. Then, the process of Rational Polynomial Coefficients (RPC) block adjustment is performed to compensate the RPC bias errors. After that, a dense matching process is performed to generate 3D point clouds for each pair of input HRS images using a new geometric transformation called PWB (pricewise bilinear) and an accurate area-based matching method called SWLSM (selective weighted least square matching). The key innovations of this research include the introduction of SWLSM and PWB methods for an accurate dense matching process. The PWB is a novel and simple piecewise geometric transformation model based on superpixel over-segmentation that has been proposed for accurate registration of each pair of HRS images. The SWLSM matching method is based on phase congruency measure and a selection strategy to improve the well-known LSM (least square matching) performance. After dense matching process, the final stage is spatial intersection to generate 3D point clouds, followed by elevation interpolation to produce DSM. To evaluate the Sat-DSM method, 12 sets of MVS-HRS data from IRS-P5, ZY3-1, ZY3-2, and Worldview-3 sensors were selected from areas with different landscapes such as urban, mountainous, and agricultural areas. The results indicate the superiority of the proposed Sat-DSM method over four other methods CATALYST, SGM (Semi-global matching), SS-DSM (structural similarity based DSM extraction), and Sat-MVSF in terms of completeness, RMSE, and MEE. The demo code is available at https://www.researchgate.net/publication/377721674_SatDSM.

MATCHING FILTER-BASED VSLAM OPTIMIZATION IN INDOOR ENVIRONMENTS

Abstract. An important factor that reduced the accuracy of motion trajectories in existing VSLAM (Visual Simultaneous Localization and Mapping) systems is the poor estimation of the position pose of the vision odometer. The existing methods generate many incorrect matches during the feature matching process, resulting in low computational accuracy of rotations and translations between cameras, which further leads to a reduction in the robustness of the overall system. In addition, the sparse feature point maps do not provide a detailed description of the surrounding environment, which makes it difficult for the devices equipped with VSLAM systems to perform advanced tasks such as navigation, path planning and human-computer interaction. To address the accuracy problem, we select the set of matches from existing feature matching algorithms based on the motion consistency constraint and use a random sampling consistency algorithm to obtain the best quality matches from the selected samples for computing the geometric transformation model and estimating the current pose. To address the problem of sparse map points, we use the depth information from the RGB-D or Stereo camera to build a dense map module to ensure that information about the surrounding environment is recorded as a point cloud, which provides data support for the implementation of advanced tasks of the device.

Automated Georectification, Mosaicking and 3D Point Cloud Generation Using UAV-Based Hyperspectral Imagery Observed by Line Scanner Imaging Sensors

Hyperspectral sensors mounted on unmanned aerial vehicles (UAV) offer the prospect of high-resolution multi-temporal spectral analysis for a range of remote-sensing applications. However, although accurate onboard navigation sensors track the moment-to-moment pose of the UAV in flight, geometric distortions are introduced into the scanned data sets. Consequently, considerable time-consuming (user/manual) post-processing rectification effort is generally required to retrieve geometrically accurate mosaics of the hyperspectral data cubes. Moreover, due to the line-scan nature of many hyperspectral sensors and their intrinsic inability to exploit structure from motion (SfM), only 2D mosaics are generally created. To address this, we propose a fast, automated and computationally robust georectification and mosaicking technique that generates 3D hyperspectral point clouds. The technique first morphologically and geometrically examines (and, if possible, repairs) poorly constructed individual hyperspectral cubes before aligning these cubes into swaths. The luminance of each individual cube is estimated and normalised, prior to being integrated into a swath of images. The hyperspectral swaths are co-registered to a targeted element of a luminance-normalised orthomosaic obtained using a standard red–green–blue (RGB) camera and SfM. To avoid computationally intensive image processing operations such as 2D convolutions, key elements of the orthomosaic are identified using pixel masks, pixel index manipulation and nearest neighbour searches. Maximally stable extremal regions (MSER) and speeded-up robust feature (SURF) extraction are then combined with maximum likelihood sample consensus (MLESAC) feature matching to generate the best geometric transformation model for each swath. This geometrically transforms and merges individual pushbroom scanlines into a single spatially continuous hyperspectral mosaic; and this georectified 2D hyperspectral mosaic is then converted into a 3D hyperspectral point cloud by aligning the hyperspectral mosaic with the RGB point cloud used to create the orthomosaic obtained using SfM. A high spatial accuracy is demonstrated. Hyperspectral mosaics with a 5 cm spatial resolution were mosaicked with root mean square positional accuracies of 0.42 m. The technique was tested on five scenes comprising two types of landscape. The entire process, which is coded in MATLAB, takes around twenty minutes to process data sets covering around 30 Ha at a 5 cm resolution on a laptop with 32 GB RAM and an Intel® Core i7-8850H CPU running at 2.60 GHz.

A general geometric transformation model for line-scan image registration

A reasonable geometric transformation model is the key to image registration. When the relative motion direction between the line-scan camera and the object is strictly parallel to the planar object, it is possible to align the image by using the eight-parameter geometric transformation model of the line-scan image. However, it will be invalid when the relative motion direction is arbitrary. Therefore, a new general geometric transformation model of line-scan images is proposed for line-scan image registration in this paper. Considering the different initial poses and motion directions of the line-scan camera, the proposed model is established based on the imaging model of the line-scan camera. In order to acquire line-scan images to verify the proposed model, a line-scan image acquisition system was built. The method based on feature points is used to register the line-scan images. The experimental results show that the proposed geometric transformation model can align the line-scan image collected under arbitrary relative motion direction, not just the parallel case. Besides, the statistical errors of the image feature point coordinates are the best performance after registration. The accuracy of the registration results is better than that of other existing geometric transformation models, which verifies the correctness and generality of the geometric transformation model of the line-scan camera proposed in this paper.

A methodology for transformation between geodetic datums used in Syria

The Global Navigation Satellite System (GNSS) provides geodetic coordinates referenced to the world ellipsoid WGS84, whereas positions are computed on the local ellipsoid adopted for geodetic computation in various countries. Thus, to take advantage of GNSS in geodetic applications, the need arises to transform coordinates from the global ellipsoid to the local ellipsoid. This is usually carried out by applying geometric transformation models to convert coordinates from The Global Geodetic System WGS84 into the local systems used in each country of the world and into the grid coordinates of local systems. Over the past years, with the increase in the utilization of GNSS in several geodetic projects, numerous methodologies are available for the solution of transformation issues. These methods can be classified into three categories, the first is based on direct mathematical formulas, the second on iteration approaches, and the last category depends on direct transformation from the global system to the local system based on the calculation of transformation parameters (three, seven, nine …etc.). In this study, a methodology has been proposed for direct transformation from global geodetic coordinates to local geodetic coordinates for a limited area in Syria. It is based on the properties of the ratio and proportion between the geometric elements of points on global and local ellipsoids without the calculation of transformation parameters. The results are compared with some studied methods (Bursa-Wolf, Molodensky Abridge, and Cassini). The resulting accuracy is about ± 3.5 cm. The main conclusion drawn is that the proposed method provides a promising alternative approach in coordinates transformation. Therefore, the capability of the suggested methodology as a powerful method for converting geodetic coordinates from one referenced frame to another has been demonstrated in this present study.

Contrastive Feature Disentangling for Partial Aspect Angles SAR Noncooperative Target Recognition

Deep learning algorithms have achieved state-of-the-art progress in synthetic aperture radar (SAR) automatic target recognition (ATR) tasks. They theoretically assume that training and test samples are independent and identically distributed (i.i.d.) for generalization, but it is intractable for practical ATR scenarios. In this paper, we propose a novel contrastive feature disentangling framework termed ConFeDent to learn features with improved generalization performance under a condition of a weaker distribution consistency. More specifically, ConFeDent aims to describe the semantic interactions between two arbitrary SAR training samples instead of treating them independently. It can implicitly disentangle features encoding the pose and identity knowledge from the whole samples with a semi-parametric geometric transformation model and a second-order energy model. In particular, except for the identity label, we use deductive-based geometry knowledge as supervision to teach the model to learn the concept of aspect angle variation. A progressively amortized inference scheme is constructed for efficient feature learning and recognition in an end-to-end manner. Finally, we further release a strengthened version, called ConFeDent+, which can explicitly utilize and learn more information from cross-category samples. Experimental results on the moving and stationary target acquisition and recognition (MSTAR) benchmark demonstrate the effectiveness of our proposed models in the SAR ATR. In particular, we validate the algorithms in a more challenging scenario where the range of aspect angles for training and testing samples is permitted to be disparate. Our model can achieve much higher recognition accuracy than other SAR ATR algorithms.

Geometric transformation modeling for line-scan images under different camera poses

Geometrically aligning two images is a basic image process in the machine vision field. In general, the geometric transformation model is selected according to the complexity of the geometric transformation between images, and the type of camera that takes the image is ignored. However, there are essential differences in the imaging mechanism between a line-scan camera and an area array camera, so the geometric transformation model of the area array image does not conform to the geometric transformation of the line-scan image. Therefore, according to the imaging model of the line-scan camera, we established the geometric transformation model of line-scan images. A line-scan image acquisition system was built, and planar objects were imaged by different line-scan camera poses. Then, taking the proposed model as the geometric transformation model, the line-scan images collected in this paper and the line-scan images of realistic application were respectively registered. As a contrast experiment, the homography of the area array image was adopted as the geometric transformation model to align these images again. Through the comparison of registration results, the correctness of the proposed geometric transformation model of line-scan images was verified, and the accuracy of the line-scan image registration was improved.

CAISOV: Collinear Affine Invariance and Scale-Orientation Voting for Reliable Feature Matching

Reliable feature matching plays an important role in the fields of computer vision and photogrammetry. Due to the complex transformation model caused by photometric and geometric deformations, and the limited discriminative power of local feature descriptors, initial matches with high outlier ratios cannot be addressed very well. This study proposes a reliable outlier-removal algorithm by combining two affine-invariant geometric constraints. First, a very simple geometric constraint, namely, CAI (collinear affine invariance) has been implemented, which is based on the observation that the collinear property of any two points is invariant under affine transformation. Second, after the first-step outlier removal based on the CAI constraint, the SOV (scale-orientation voting) scheme was then adopted to remove remaining outliers and recover the lost inliers, in which the peaks of both scale and orientation voting define the parameters of the geometric transformation model. Finally, match expansion was executed using the Delaunay triangulation of refined matches. By using close-range (rigid and non-rigid images) and UAV (unmanned aerial vehicle) datasets, comprehensive comparison and analysis are conducted in this study. The results demonstrate that the proposed outlier-removal algorithm achieves the best overall performance when compared with RANSAC-like and local geometric constraint-based methods, and it can also be applied to achieve reliable outlier removal in the workflow of SfM-based UAV image orientation.

Accuracy improvement of linear stages using on-machine geometric error measurement system and error transformation model.

In order to improve the accuracy of linear stages, a compact, portable and easy installation of a six-degree-of-freedom (6DOF) geometric error measurement system, in which two mirrors with special position and orientation are innovatively regarded as the sensitive elements of the roll error, is proposed. A set of combined focus lenses is integrated into the 6DOF measurement system to improve the resolution of the roll error. The accuracy of a linear stage is evaluated by the positional errors at the functional point, which is located at the working volume of a linear stage. An error transformation model based on the Abbe principle and the Bryan principle is established to estimate the positional errors at the functional point according to those at the measurement point. A series of experiments are carried out to verify the capable of the designed system and the effectiveness of the established model.

Combining Optimized SAR-SIFT Features and RD Model for Multisource SAR Image Registration

Multisource synthetic aperture radar (SAR) image registration is a difficult task in remote sensing due to the influence of speckle noise and geometric distortions between the images. The SAR scale-invariant feature transform (SAR-SIFT) is a prior option to extract features for automatic registration of SAR images. However, most registration methods based on the SAR-SIFT operator only use the image information (the geometric distribution of the pixels making up the feature) and do not consider the influences of the side-looking imagery mode and the topographic relief. In this article, we propose a novel registration method to address the above problems, which combines the optimized SAR-SIFT features and the imaging geometry of the SAR system. The major improvement of the algorithm is the use of the range–Doppler (RD) model in feature matching and geometric transformation estimation. A new local matching method aided by the RD model, transforming the global matching mode to the local one, is first introduced to avoid the gross errors and improve the matching efficiency. Then, an RD-model-based geometric transformation model is presented for multisource SAR images with obvious geometric distortions. In addition, the stationary wavelet transform (SWT) is brought in the feature extraction to optimize the SAR-SIFT keypoints to extract reliable and uniformed features. The experimental results fully demonstrate the applicability of the proposed method for registration of multisource SAR images in different acquiring configurations, especially with different passes.

Experimental research on a precise terrain reconstruction algorithm based on stereo images for the Chang’E-5 lunar sample return mission

<p indent=0mm>An exact terrain reconstruction algorithm based on stereo images is proposed for the Chang’E-5 lunar sample return mission. First, the paper presents a brief introduction to the proposed algorithm. Then, an analysis of the factors influencing terrain reconstruction and the geometric transformation model between the reconstructed and measured points are described. Finally, some conclusions drawn based on the experiment results of China’s Chang’E-5 probe system are presented. The approved algorithm can achieve high accuracy (the mean distance between the reconstructed and measured points was less than <sc>6.0 mm,</sc> and the variance was less than <sc>13.0 mm)</sc> and stability for terrain reconstruction.

Method of extracting control points from GF-7 remote sensing image based on five-layer-fifteen-level tiles

In view of the traditional manual and semi-automatic methods can not quickly and effectively extract control points, this paper uses five-layer fifteen-level tiles (FLFLT) as reference images, and proposes an efficient and automatic method for automatic extraction control points of GF-7 image. Firstly, the remote sensing image and reference image are sampled down, and the remote sensing image is partitioned to improve the image processing efficiency. The Harris algorithm is used to extract the feature points of the remote sensing image and reference image, the normalized cross-correlation (NCC) algorithm is used for feature matching, and the Random Sampling Consistent (RANSAC) algorithm is used for gross error elimination. Finally, the least-square algorithm was used to fit the geometric transformation parameters, and the geometric transformation model was used to carry out geometric correction of GF-7 image, and the better correction results were achieved. Experimental results show that the proposed algorithm can extract control points quickly and effectively, and can be used for automatic extraction and geometric correction of high resolution satellite data.

Improving Co-Registration for Sentinel-1 SAR and Sentinel-2 Optical Images

Co-registering the Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical data of the European Space Agency (ESA) is of great importance for many remote sensing applications. However, we find that there are evident misregistration shifts between the Sentinel-1 SAR and Sentinel-2 optical images that are directly downloaded from the official website. To address that, this paper presents a fast and effective registration method for the two types of images. In the proposed method, a block-based scheme is first designed to extract evenly distributed interest points. Then, the correspondences are detected by using the similarity of structural features between the SAR and optical images, where the three-dimensional (3D) phase correlation (PC) is used as the similarity measure for accelerating image matching. Lastly, the obtained correspondences are employed to measure the misregistration shifts between the images. Moreover, to eliminate the misregistration, we use some representative geometric transformation models such as polynomial models, projective models, and rational function models for the co-registration of the two types of images, and we compare and analyze their registration accuracy under different numbers of control points and different terrains. Six pairs of the Sentinel-1 SAR L1 and Sentinel-2 optical L1C images covering three different terrains are tested in our experiments. Experimental results show that the proposed method can achieve precise correspondences between the images, and the third-order polynomial achieves the most satisfactory registration results. Its registration accuracy of the flat areas is less than 1.0 10 m pixel, that of the hilly areas is about 1.5 10 m pixels, and that of the mountainous areas is between 1.7 and 2.3 10 m pixels, which significantly improves the co-registration accuracy of the Sentinel-1 SAR and Sentinel-2 optical images.

A Learnable Joint Spatial and Spectral Transformation for High Resolution Remote Sensing Image Retrieval

Geometric and spectral distortions of remote sensing images are key obstacles for deep learning-based supervised classification and retrieval, which are worsened by cross-dataset applications. A learnable geometric transformation model imbedded in a deep learning model has been used as a tool for handling geometric distortions to process close-range images with different view angles. However, a learnable spectral transformation model, which is more noteworthy in remote image processing, has not yet been designed and explored up to now. In this paper, we propose a learnable joint spatial and spectral transformation (JSST) model for remote sensing image retrieval (RSIR), which is composed of three modules: a parameter generation network (PGN); a spatial conversion module; and a spectral conversion module. The PGN adaptively learns the geometric and spectral transformation parameters simultaneously from the different input image content, and these parameters then guide the spatial and spectral conversions to produce a new modified image with geometric and spectral correction. Our learnable JSST is imbedded in the front-end of the deep-learning-based retrieval network. The spatial and spectral-modified inputs provided by the JSST endow the retrieval network with better generalization and adaptation ability for cross-dataset RSIR. Our experiments on four open-source RSIR datasets confirmed that our proposed JSST embedded retrieval network outperformed state-of-the-art approaches comprehensively.

An Efficient Method for Generating UAV-Based Hyperspectral Mosaics Using Push-Broom Sensors

Hyperspectral sensors mounted in unmanned aerial vehicles offer new opportunities to explore high-resolution multitemporal spectral analysis in remote sensing applications. Nevertheless, the use of hyperspectral data still poses challenges mainly in postprocessing to correct from high geometric deformation of images. In general, the acquisition of high-quality hyperspectral imagery is achieved through a time-consuming and complex processing workflow. However, this effort is mandatory when using hyperspectral imagery in a multisensor data fusion perspective, such as with thermal infrared imagery or photogrammetric point clouds. Push-broom hyperspectral sensors provide high spectral resolution data, but its scanning acquisition architecture imposes more challenges to create geometrically accurate mosaics from multiple hyperspectral swaths. In this article, an efficient method is presented to correct geometrical distortions on hyperspectral swaths from push-broom sensors by aligning them with an RGB photogrammetric orthophoto mosaic. The proposed method is based on an iterative approach to align hyperspectral swaths with an RGB photogrammetric orthophoto mosaic. Using as input preprocessed hyperspectral swaths, apart from the need of introducing some control points, the workflow is fully automatic and consists of: adaptive swath subdivision into multiple fragments; detection of significant image features; estimation of valid matches between individual swaths and the RGB orthophoto mosaic; and calculation of the best geometric transformation model to the retrieved matches. As a result, geometrical distortions of hyperspectral swaths are corrected and an orthomosaic is generated. This methodology provides an expedite solution able to produce a hyperspectral mosaic with an accuracy ranging from two to five times the ground sampling distance of the high-resolution RGB orthophoto mosaic, enabling the hyperspectral data integration with data from other sensors for multiple applications.

An Approach towards Increasing Prediction Accuracy for the Recovery of Missing IoT Data Based on the GRNN-SGTM Ensemble.

The purpose of this paper is to improve the accuracy of solving prediction tasks of the missing IoT data recovery. To achieve this, the authors have developed a new ensemble of neural network tools. It consists of two successive General Regression Neural Network (GRNN) networks and one neural-like structure of the Successive Geometric Transformation Model (SGTM). The principle of ensemble topology construction on two successively connected general regression neural networks, supplemented with an SGTM neural-like structure, is mathematically substantiated, which improves the accuracy of prediction results. The effectiveness of the method is based on the replacement of the summation of the results of the two GRNNs with a weighted summation, which improves the accuracy of the ensemble operation in general. A detailed algorithmic implementation of the ensemble method as well as a flowchart of its operation is presented. The parameters of the ensemble operation are determined by optimization using the brute-force method. Based on the developed ensemble method, the solution of the task of completing the partially missing values in the real monitoring dataset of the air environment collected by the IoT device is presented. By comparing the performance of the developed ensemble with the existing methods, the highest accuracy of its performance (by the parameters of Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) accuracy) among the most similar in this class has been proved.

Rapid Defect Detection for Spacecraft in Infrared Reconstructed Images Using Image Mosaic Technique

In order to satisfy the real-time and large scale requirement of spacecraft nondestructive testing using optical pulsed thermography, in this research, we propose a novel approach based on image mosaic technique which can create large scale or panoramic image mosaics from a set of ordered infrared reconstruction images with overlapping areas for defect detection. First, infrared reconstruction images are extracted from the original thermal video stream by the ICA method based on the idea of blind source signal processing. Then for this research’s special mosaic object, a fast and accurate registration scheme is proposed. Abundant scale rotation invariant feature points are quickly obtained by using the hybrid feature detector and descriptor. Moreover, the feature matching process is realized by applying two-way FLANN and MSAC, parameters of the geometric transformation matrix are estimated by MSAC, and image mosaic is realized according to the geometric transformation model. The experimental results convinced the validity and efficiency of the proposed method.

Ансамбль нейромереж GRNN на підставі зміщених поверхонь відгуку для задач електронної комерції

Розв'язок задач електронної комерції, які здебільшого характеризуються нелінійними поверхнями відгуку, є важливим завданням. Застосування сучасних засобів обчислювального інтелекту не завжди є доречним зважаючи на складність реалізації процедур навчання і налагодження. Неітеративні засоби та нейронні мережі без навчання також не забезпечують задовільної точності результату. З огляду на це у роботі описано новий ансамбль на підставі нейронних мереж узагальненої регресії. Основна ідея розробленого ансамблю полягає в лінеаризації поверхні відгуку, що задається даними наявної вибірки. Для цього отримана за допомогою мережі GRNN поверхня подається на вхід лінійної нейроподібної структури. Така комбінація забезпечує підвищення точності роботи ансамблю під час розв'язання поставленої задачі. Описаний ансамбль застосовано для розв'язання задачі прогнозування ціни на вживані автомобілі. Експериментальним способом підібрано оптимальні параметри його роботи. Шляхом порівняння із відомими методами встановлено найвищу точність його роботи. Результати експериментальних досліджень порівняно з теоретичними оцінками на підставі висновків теореми Кондорсе про журі присяжних. Розроблений ансамбль нейронних мереж узагальненої регресії на підставі зміщення поверхонь відгуку та з додатковим використанням нейроподібних структур Моделі послідовних геометричних перетворень варто застосовувати під час розв'язання різноманітних задач електронної комерції підвищеної точності.

Automated Georectification and Mosaicking of UAV-Based Hyperspectral Imagery from Push-Broom Sensors

Hyperspectral systems integrated on unmanned aerial vehicles (UAV) provide unique opportunities to conduct high-resolution multitemporal spectral analysis for diverse applications. However, additional time-consuming rectification efforts in postprocessing are routinely required, since geometric distortions can be introduced due to UAV movements during flight, even if navigation/motion sensors are used to track the position of each scan. Part of the challenge in obtaining high-quality imagery relates to the lack of a fast processing workflow that can retrieve geometrically accurate mosaics while optimizing the ground data collection efforts. To address this problem, we explored a computationally robust automated georectification and mosaicking methodology. It operates effectively in a parallel computing environment and evaluates results against a number of high-spatial-resolution datasets (mm to cm resolution) collected using a push-broom sensor and an associated RGB frame-based camera. The methodology estimates the luminance of the hyperspectral swaths and coregisters these against a luminance RGB-based orthophoto. The procedure includes an improved coregistration strategy by integrating the Speeded-Up Robust Features (SURF) algorithm, with the Maximum Likelihood Estimator Sample Consensus (MLESAC) approach. SURF identifies common features between each swath and the RGB-orthomosaic, while MLESAC fits the best geometric transformation model to the retrieved matches. Individual scanlines are then geometrically transformed and merged into a single spatially continuous mosaic reaching high positional accuracies only with a few number of ground control points (GCPs). The capacity of the workflow to achieve high spatial accuracy was demonstrated by examining statistical metrics such as RMSE, MAE, and the relative positional accuracy at 95% confidence level. Comparison against a user-generated georectification demonstrates that the automated approach speeds up the coregistration process by 85%.

Development of the method of forecasting the atmospheric air pollution parameters based on error correction by neural-like structures of the model of successive geometric transformations

The article describes the importance of improving existing and exploring new algorithms for predicting environmental parameters to improve the quality of environmental monitoring. Because the organization and management of production require the development of new approaches to the problem of control and management of industrial sources of harmful substances based on new information technologies. One of the most problematic places in industrial air quality control and management systems is the development of advanced prospective air pollution forecasting algorithms. These algorithms must take into account t situational changes in data distribution and do not require retraining of atmospheric air pollution parameters. With the advent of neural-like structures, there is a need for their study, including the task of predicting the parameters of air pollution. The object of research is the neural-like structures of the Model of Successive Geometric Transformations. A method for predicting the parameters of atmospheric air pollution based on error correction with the help of a committee of different types of neural-like structures is proposed. In the course of the study, three methods for predicting the parameters of atmospheric air pollution were analyzed: a Generalized Regression Neural Network, a Radial Basis Function, and a neural-like structure of Sequential Geometric Transformations Model. A combination of these methods was performed and the results of the three methods were compared. It is experimentally determined that the prediction of atmospheric air pollution parameters based on the error correction using the committee of neural-like structures of the Sequential Geometric Transformations Model provides a prediction error reduction by 7 % of the General Regression Neural Network and by 2.6 % of the Radial Basis Function with extended General Regression Network. The obtained results increase the reliability and speed of forecasting of atmospheric air parameters to improve the quality of monitoring of emissions of harmful impurities in production and to make environmental management decisions.