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Michal Kedzierski
Institute of Geospatial Engineering and Geodesy, Faculty of Civil Engineering and Geodesy, Military University of Technology, 08-521 Dęblin, Poland

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Journal article
Published: 13 October 2020 in Remote Sensing
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Unmanned aerial vehicle (UAV) systems are often used to collect high-resolution imagery. Data obtained from UAVs are now widely used for both military and civilian purposes. This article discusses the issues related to the use of UAVs for the imaging of restricted areas. Two methods of developing single-strip blocks with the optimal number of ground control points are presented. The proposed methodology is based on a modified linear regression model and an empirically modified Levenberg–Marquardt–Powell algorithm. The effectiveness of the proposed methods of adjusting a single-strip block were verified based on several test sets. For method I, the mean square errors (RMSE) values for the X, Y, Z coordinates of the control points were within the range of 0.03–0.13 m/0.08–0.09 m, and for the second method, 0.03–0.04 m/0.06–0.07 m. For independent control points, the RMSE values were 0.07–0.12 m/0.06–0.07 m for the first method and 0.07–0.12 m/0.07–0.09 m for the second method. The results of the single-strip block adjustment showed that the use of the modified Levenberg–Marquardt–Powell method improved the adjustment accuracy by 13% and 16%, respectively.

ACS Style

Marta Lalak; Damian Wierzbicki; Michał Kędzierski. Methodology of Processing Single-Strip Blocks of Imagery with Reduction and Optimization Number of Ground Control Points in UAV Photogrammetry. Remote Sensing 2020, 12, 3336 .

AMA Style

Marta Lalak, Damian Wierzbicki, Michał Kędzierski. Methodology of Processing Single-Strip Blocks of Imagery with Reduction and Optimization Number of Ground Control Points in UAV Photogrammetry. Remote Sensing. 2020; 12 (20):3336.

Chicago/Turabian Style

Marta Lalak; Damian Wierzbicki; Michał Kędzierski. 2020. "Methodology of Processing Single-Strip Blocks of Imagery with Reduction and Optimization Number of Ground Control Points in UAV Photogrammetry." Remote Sensing 12, no. 20: 3336.

Journal article
Published: 28 July 2020 in Measurement
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The paper discusses the issue of using artificial neural networks and point clouds for calculating displacements of cultural heritage structures. The model trained on laboratory dataset was able to determine displacements of the building façade, with the relative accuracy of 3% of the simulated values. The success rate of this model was equal to 85%. The deformations that were derived from digital surface models generated from point clouds, had the relative accuracy of 7% and the values determined by image based close-range photogrammetry methods - 35%. A major novelty is the use of neural networks to determine deformations based on sub-models generated from the point cloud and the unique, supervised-trained, high accuracy predictive model. Practical significance is associated with creating an end-to-end solution that performs detection and estimates the value of the deformation automatically which is a major advantage over other methods.

ACS Style

Michalina Wojtkowska; Michal Kedzierski; Paulina Delis. Validation of terrestrial laser scanning and artificial intelligence for measuring deformations of cultural heritage structures. Measurement 2020, 167, 108291 .

AMA Style

Michalina Wojtkowska, Michal Kedzierski, Paulina Delis. Validation of terrestrial laser scanning and artificial intelligence for measuring deformations of cultural heritage structures. Measurement. 2020; 167 ():108291.

Chicago/Turabian Style

Michalina Wojtkowska; Michal Kedzierski; Paulina Delis. 2020. "Validation of terrestrial laser scanning and artificial intelligence for measuring deformations of cultural heritage structures." Measurement 167, no. : 108291.

Journal article
Published: 13 July 2020 in Remote Sensing
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With the development of effective deep learning algorithms, it became possible to achieve high accuracy when conducting remote sensing analyses on very high-resolution images (VHRS), especially in the context of building detection and classification. In this article, in order to improve the accuracy of building detection and classification, we propose a Faster Edge Region Convolutional Neural Networks (FER-CNN) algorithm. This proposed algorithm is trained and evaluated on different datasets. In addition, we propose a new method to improve the detection of the boundaries of detected buildings. The results of our algorithm are compared with those of other methods, such as classical Faster Region Convolution Neural Network (Faster R-CNN) with the original VGG16 and the Single-Shot Multibox Detector (SSD). The experimental results show that our methods make it possible to obtain an average detection accuracy of 97.5% with a false positive classification rate of 8.4%. An additional advantage of our method is better resistance to shadows, which is a very common issue for satellite images of urban areas. Future research will include designing and training the neural network to detect small buildings, as well as irregularly shaped buildings that are partially obscured by shadows or other occlusions.

ACS Style

Kinga Reda; Michal Kedzierski. Detection, Classification and Boundary Regularization of Buildings in Satellite Imagery Using Faster Edge Region Convolutional Neural Networks. Remote Sensing 2020, 12, 2240 .

AMA Style

Kinga Reda, Michal Kedzierski. Detection, Classification and Boundary Regularization of Buildings in Satellite Imagery Using Faster Edge Region Convolutional Neural Networks. Remote Sensing. 2020; 12 (14):2240.

Chicago/Turabian Style

Kinga Reda; Michal Kedzierski. 2020. "Detection, Classification and Boundary Regularization of Buildings in Satellite Imagery Using Faster Edge Region Convolutional Neural Networks." Remote Sensing 12, no. 14: 2240.

Journal article
Published: 24 March 2020 in Remote Sensing
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Images acquired at a low altitude can be the source of accurate information about various environmental phenomena. Often, however, this information is distorted by various factors, so a correction of the images needs to be performed to recreate the actual reflective properties of the imaged area. Due to the low flight altitude, the correction of images from UAVs (unmanned aerial vehicles) is usually limited to noise reduction and detector errors. The article shows the influence of the Sun position and platform deviation angles on the quality of images obtained by UAVs. Tilting the camera placed on an unmanned platform leads to incorrect exposures of imagery, and the order of this distortion depends on the position of the Sun during imaging. An image can be considered in three-dimensional space, where the x and y coordinates determine the position of the pixel and the third dimension determines its exposure. This assumption is the basis for the proposed method of image exposure compensation. A three-dimensional transformation by rotation is used to determine the adjustment matrix to correct the image quality. The adjustments depend on the angles of the platform and the difference between the direction of flight and the position of the Sun. An additional factor regulates the value of the adjustment depending on the ratio of the pitch and roll angles. The experiments were carried out for two sets of data obtained with different unmanned systems. The correction method used can improve the block exposure by up to 60%. The method gives the best results for simple systems, not equipped with lighting compensation systems.

ACS Style

Aleksandra Sekrecka; Damian Wierzbicki; Michal Kedzierski. Influence of the Sun Position and Platform Orientation on the Quality of Imagery Obtained from Unmanned Aerial Vehicles. Remote Sensing 2020, 12, 1040 .

AMA Style

Aleksandra Sekrecka, Damian Wierzbicki, Michal Kedzierski. Influence of the Sun Position and Platform Orientation on the Quality of Imagery Obtained from Unmanned Aerial Vehicles. Remote Sensing. 2020; 12 (6):1040.

Chicago/Turabian Style

Aleksandra Sekrecka; Damian Wierzbicki; Michal Kedzierski. 2020. "Influence of the Sun Position and Platform Orientation on the Quality of Imagery Obtained from Unmanned Aerial Vehicles." Remote Sensing 12, no. 6: 1040.

Journal article
Published: 19 December 2019 in Remote Sensing
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Unmanned aerial vehicles (UAVs) equipped with compact digital cameras and multi-spectral sensors are used in remote sensing applications and environmental studies. Recently, due to the reduction of costs of these types of system, the increase in their reliability, and the possibility of image acquisition with very high spatial resolution, low altitudes imaging is used in many qualitative and quantitative analyses in remote sensing. Also, there has been an enormous development in the processing of images obtained with UAV platforms. Until now, research on UAV imaging has focused mainly on aspects of geometric and partially radiometric correction. And consideration of the effects of low atmosphere and haze on images has so far been neglected due to the low operating altitudes of UAVs. However, it proved to be the case that the path of sunlight passing through various layers of the low atmosphere causes refraction and causes incorrect registration of reflection by the imaging sensor. Images obtained from low altitudes may be degraded due to the scattering process caused by fog and weather conditions. These negative atmospheric factors cause a reduction in contrast and colour reproduction in the image, thereby reducing its radiometric quality. This paper presents a method of dehazing images acquired with UAV platforms. As part of the research, a methodology for imagery acquisition from a low altitude was introduced, and methods of atmospheric calibration based on the atmosphere scattering model were presented. Moreover, a modified dehazing model using Wiener’s adaptive filter was presented. The accuracy assessment of the proposed dehazing method was made using qualitative indices such as structural similarity (SSIM), peak signal to noise ratio (PSNR), root mean square error (RMSE), Correlation Coefficient, Universal Image Quality Index (Q index) and Entropy. The experimental results showed that using the proposed dehazing method allowed the removal of the negative impact of haze and improved image quality, based on the PSNR index, even by an average of 34% compared to other similar methods. The obtained results show that our approach allows processing of the images to remove the negative impact of the low atmosphere. Thanks to this technique, it is possible to obtain a dehazing effect on images acquired at high humidity and radiation fog. The results from this study can provide better quality images for remote sensing analysis.

ACS Style

Damian Wierzbicki; Michal Kedzierski; Aleksandra Sekrecka. A Method for Dehazing Images Obtained from Low Altitudes during High-Pressure Fronts. Remote Sensing 2019, 12, 25 .

AMA Style

Damian Wierzbicki, Michal Kedzierski, Aleksandra Sekrecka. A Method for Dehazing Images Obtained from Low Altitudes during High-Pressure Fronts. Remote Sensing. 2019; 12 (1):25.

Chicago/Turabian Style

Damian Wierzbicki; Michal Kedzierski; Aleksandra Sekrecka. 2019. "A Method for Dehazing Images Obtained from Low Altitudes during High-Pressure Fronts." Remote Sensing 12, no. 1: 25.

Journal article
Published: 24 November 2019 in Sensors
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In recent years, many techniques of fusion of multi-sensors satellite images have been developed. This article focuses on examining and improvement the usability of pansharpened images for object detection, especially when fusing data with a high GSD ratio. A methodology to improve an interpretative ability of pansharpening results is based on pre-processing of the panchromatic image using Logarithmic-Laplace filtration. The proposed approach was used to examine several different pansharpening methods and data sets with different spatial resolution ratios, i.e., from 1:4 to 1:60. The obtained results showed that the proposed approach significantly improves an object detection of fused images, especially for imagery data with a high-resolution ratio. The interpretative ability was assessed using qualitative method (based on image segmentation) and quantitative method (using an indicator based on the Speeded Up Robust Features (SURF) detector). In the case of combining data acquired with the same sensor the interpretative potential had improved by a dozen or so per cent. However, for data with a high resolution ratio, the improvement was several dozen, or even several hundred per cents, in the case of images blurred after pansharpening by the classic method (with original panchromatic image). Image segmentation showed that it is possible to recognize narrow objects that were originally blurred and difficult to identify. In addition, for panchromatic images acquired by WorldView-2, the proposed approach improved not only object detection but also the spectral quality of the fused image.

ACS Style

Aleksandra Sekrecka; Michal Kedzierski; Damian Wierzbicki. Pre-Processing of Panchromatic Images to Improve Object Detection in Pansharpened Images. Sensors 2019, 19, 5146 .

AMA Style

Aleksandra Sekrecka, Michal Kedzierski, Damian Wierzbicki. Pre-Processing of Panchromatic Images to Improve Object Detection in Pansharpened Images. Sensors. 2019; 19 (23):5146.

Chicago/Turabian Style

Aleksandra Sekrecka; Michal Kedzierski; Damian Wierzbicki. 2019. "Pre-Processing of Panchromatic Images to Improve Object Detection in Pansharpened Images." Sensors 19, no. 23: 5146.

Journal article
Published: 22 May 2019 in Remote Sensing
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Unmanned aerial vehicle (UAV) imagery has been widely used in remote sensing and photogrammetry for some time. Increasingly often, apart from recording images in the red-green-blue (RGB) range, multispectral images are also recorded. It is important to accurately assess the radiometric quality of UAV imagery to eliminate interference that might reduce the interpretation potential of the images and distort the results of remote sensing analyses. Such assessment should consider the influence of the atmosphere and the seasonal and weather conditions at the time of acquiring the imagery. The assessment of the radiometric quality of images acquired in different weather conditions is crucial in terms of improving the interpretation potential of the imagery and improving the accuracy of determining the indicators used in remote sensing and in environmental monitoring. Until now, the assessment of radiometric quality of UAV imagery did not consider the influence of meteorological conditions at different times of year. This paper presents an assessment of the influence of weather conditions on the quality of UAV imagery acquired in the visible range. This study presents the methodology for assessing image quality, considering the weather conditions characteristic of autumn in Central and Eastern Europe. The proposed solution facilitates the assessment of the radiometric quality of images acquired in the visible range. Using the objective indicator of quality assessment developed in this study, images were classified into appropriate categories, allowing, at a later stage, to improve the results of vegetation indices. The obtained results confirm that the proposed quality assessment methodology enables the objective assessment of the quality of imagery acquired in different meteorological conditions.

ACS Style

Michal Kedzierski; Damian Wierzbicki; Aleksandra Sekrecka; Anna Fryskowska; Piotr Walczykowski; Jolanta Siewert. Influence of Lower Atmosphere on the Radiometric Quality of Unmanned Aerial Vehicle Imagery. Remote Sensing 2019, 11, 1214 .

AMA Style

Michal Kedzierski, Damian Wierzbicki, Aleksandra Sekrecka, Anna Fryskowska, Piotr Walczykowski, Jolanta Siewert. Influence of Lower Atmosphere on the Radiometric Quality of Unmanned Aerial Vehicle Imagery. Remote Sensing. 2019; 11 (10):1214.

Chicago/Turabian Style

Michal Kedzierski; Damian Wierzbicki; Aleksandra Sekrecka; Anna Fryskowska; Piotr Walczykowski; Jolanta Siewert. 2019. "Influence of Lower Atmosphere on the Radiometric Quality of Unmanned Aerial Vehicle Imagery." Remote Sensing 11, no. 10: 1214.

Conference paper
Published: 27 March 2019 in XII Conference on Reconnaissance and Electronic Warfare Systems
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Satellite imagery obtained from open sources intelligence is of great importance for the security of the state, as the data obtained allow monitoring the location and activity of troops, as well as detecting and identifying objects and military equipment. The evaluation of the quality of these data is an important factor in their practical use. In the world literature, many authors considered the problem of image quality in terms of the spatial resolution assessment, but in this article the research concerned determination of the imagery interpretability and its improvement. The article presents the methods of evaluations and qualitative analysis of reconnaissance imagery. The analysis were carried out both by subjective and objective methods, in order to optimize the individual approach of specialists to the problem being addressed. The subjective assessment method consisted in the visual analysis of the views, which were detailed in the NIIRS. The objective evaluation was made based on the calculation of the metrics characterizing the spectral and spatial quality of the imagery. In addition, the influence of various signal processing methods was studied to improve the quality and potential of image interpretation. Radiometric amplification operations, context transformations and pansharpening were applied. The conducted research work have allowed the development of the concept of methods for improving the image quality, which resulted in their better interpretation. The use of signal processing methods (mainly radiometric amplification and high-pass filtration) resulted in improved image quality in the assessment based on both mathematical indicators and the NIIRS.

ACS Style

Piotr Walczykowski; Marcin Gorka; Michal Kedzierski; Aleksandra Sekrecka; Marcin Walkowiak. Evaluation of the interpretability of satellite imagery obtained from open sources of information. XII Conference on Reconnaissance and Electronic Warfare Systems 2019, 11055, 1105513 .

AMA Style

Piotr Walczykowski, Marcin Gorka, Michal Kedzierski, Aleksandra Sekrecka, Marcin Walkowiak. Evaluation of the interpretability of satellite imagery obtained from open sources of information. XII Conference on Reconnaissance and Electronic Warfare Systems. 2019; 11055 ():1105513.

Chicago/Turabian Style

Piotr Walczykowski; Marcin Gorka; Michal Kedzierski; Aleksandra Sekrecka; Marcin Walkowiak. 2019. "Evaluation of the interpretability of satellite imagery obtained from open sources of information." XII Conference on Reconnaissance and Electronic Warfare Systems 11055, no. : 1105513.

Conference paper
Published: 27 March 2019 in XII Conference on Reconnaissance and Electronic Warfare Systems
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The subject of study were low resolution SAR imagery, provided by Sentinel 1A and 1B, which can be obtained using open sources. Due to the great interest of products, an analysis of the interpretation possibilities of imaging acquired in interferometric wide swath mode and extra wide swath mode were made. Before exploring the possibilities, the proposed methods have been tested to improve the quality of radar images. Depending on the analyst’s needs as well as the polarization channel used, adaptive filters such as Frost, Lee-Sigma, and in some cases Gamma-Map are recommended. However, the use of classification and pseudocolor allows dividing the area of interest into basic fields, such as the urbanized area, water or vegetation. Satisfactory results were also obtained by integrating different polarization bands. The impact of incidence angle of radar beam on photographing the object was also shown.

ACS Style

Anna Fryskowska; Michal Kedzierski; Damian Wierzbicki; Marcin Gorka; Natalia Berlinska. Analysis of imagery interpretability of open sources radar satellite imagery. XII Conference on Reconnaissance and Electronic Warfare Systems 2019, 11055, 1105512 .

AMA Style

Anna Fryskowska, Michal Kedzierski, Damian Wierzbicki, Marcin Gorka, Natalia Berlinska. Analysis of imagery interpretability of open sources radar satellite imagery. XII Conference on Reconnaissance and Electronic Warfare Systems. 2019; 11055 ():1105512.

Chicago/Turabian Style

Anna Fryskowska; Michal Kedzierski; Damian Wierzbicki; Marcin Gorka; Natalia Berlinska. 2019. "Analysis of imagery interpretability of open sources radar satellite imagery." XII Conference on Reconnaissance and Electronic Warfare Systems 11055, no. : 1105512.

Journal article
Published: 13 December 2018 in Sensors
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Commonly used image fusion techniques generally produce good results for images obtained from the same sensor, with a standard ratio of spatial resolution (1:4). However, an atypical high ratio of resolution reduces the effectiveness of fusion methods resulting in a decrease in the spectral or spatial quality of the sharpened image. An important issue is the development of a method that allows for maintaining simultaneous high spatial and spectral quality. The authors propose to strengthen the pan-sharpening methods through prior modification of the panchromatic image. Local statistics of the differences between the original panchromatic image and the intensity of the multispectral image are used to detect spatial details. The Euler’s number and the distance of each pixel from the nearest pixel classified as a spatial detail determine the weight of the information collected from each integrated image. The research was carried out for several pan-sharpening methods and for data sets with different levels of spectral matching. The proposed solution allows for a greater improvement in the quality of spectral fusion, while being able to identify the same spatial details for most pan-sharpening methods and is mainly dedicated to Intensity-Hue-Saturation based methods for which the following improvements in spectral quality were achieved: about 30% for the urbanized area and about 15% for the non-urbanized area.

ACS Style

Aleksandra Sekrecka; Michal Kedzierski. Integration of Satellite Data with High Resolution Ratio: Improvement of Spectral Quality with Preserving Spatial Details. Sensors 2018, 18, 4418 .

AMA Style

Aleksandra Sekrecka, Michal Kedzierski. Integration of Satellite Data with High Resolution Ratio: Improvement of Spectral Quality with Preserving Spatial Details. Sensors. 2018; 18 (12):4418.

Chicago/Turabian Style

Aleksandra Sekrecka; Michal Kedzierski. 2018. "Integration of Satellite Data with High Resolution Ratio: Improvement of Spectral Quality with Preserving Spatial Details." Sensors 18, no. 12: 4418.

Journal article
Published: 24 August 2018 in Remote Sensing
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Imaging from low altitudes is nowadays commonly used in remote sensing and photogrammetry. More and more often, in addition to acquiring images in the visible range, images in other spectral ranges, e.g., near infrared (NIR), are also recorded. During low-altitude photogrammetric studies, small-format images of large coverage along and across the flight route are acquired that provide information about the imaged objects. The novelty presented in this research is the use of the modified method of the dark-object subtraction technique correction with a modified Walthall’s model for correction of images obtained from a low altitude. The basic versions of these models have often been used to radiometric correction of satellite imagery and classic aerial images. However, with the increasing popularity of imaging from low altitude (in particular in the NIR range), it has also become necessary to perform radiometric correction for this type of images. The radiometric correction of images acquired from low altitudes is important from the point of view of eliminating disturbances which might reduce the capabilities of image interpretation. The radiometric correction of images acquired from low altitudes should take into account the influence of the atmosphere but also the geometry of illumination, which is described by the bidirectional reflectance distribution function (BRDF). This paper presents a method of radiometric correction for unmanned aerial vehicle (UAV) NIR images. The study presents a method of low-altitude image acquisition and a fusion of the method of the dark-object subtraction technique correction with a modified Walthall’s model. The proposed solution performs the radiometric correction of images acquired in the NIR range with the root mean square error (RMSE) value not exceeding 10% with respect to the original images. The obtained results confirm that the proposed method will provide effective compensation of radiometric disturbances in UAV images.

ACS Style

Damian Wierzbicki; Michal Kedzierski; Anna Fryskowska; Janusz Jasiński. Quality Assessment of the Bidirectional Reflectance Distribution Function for NIR Imagery Sequences from UAV. Remote Sensing 2018, 10, 1348 .

AMA Style

Damian Wierzbicki, Michal Kedzierski, Anna Fryskowska, Janusz Jasiński. Quality Assessment of the Bidirectional Reflectance Distribution Function for NIR Imagery Sequences from UAV. Remote Sensing. 2018; 10 (9):1348.

Chicago/Turabian Style

Damian Wierzbicki; Michal Kedzierski; Anna Fryskowska; Janusz Jasiński. 2018. "Quality Assessment of the Bidirectional Reflectance Distribution Function for NIR Imagery Sequences from UAV." Remote Sensing 10, no. 9: 1348.

Journal article
Published: 01 January 2018 in Journal of Applied Remote Sensing
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Unmanned aerial vehicles are suited to various photogrammetry and remote sensing missions. Such platforms are equipped with various optoelectronic sensors imaging in the visible and infrared spectral ranges and also thermal sensors. Nowadays, near-infrared (NIR) images acquired from low altitudes are often used for producing orthophoto maps for precision agriculture among other things. One major problem results from the application of low-cost custom and compact NIR cameras with wide-angle lenses introducing vignetting. In numerous cases, such cameras acquire low radiometric quality images depending on the lighting conditions. The paper presents a method of radiometric quality assessment of low-altitude NIR imagery data from a custom sensor. The method utilizes statistical analysis of NIR images. The data used for the analyses were acquired from various altitudes in various weather and lighting conditions. An objective NIR imagery quality index was determined as a result of the research. The results obtained using this index enabled the classification of images into three categories: good, medium, and low radiometric quality. The classification makes it possible to determine the a priori error of the acquired images and assess whether a rerun of the photogrammetric flight is necessary.

ACS Style

Damian Wierzbicki; Anna Fryskowska; Michal Kedzierski; Michalina Wojtkowska; Paulina Delis. Method of radiometric quality assessment of NIR images acquired with a custom sensor mounted on an unmanned aerial vehicle. Journal of Applied Remote Sensing 2018, 12, 015008 .

AMA Style

Damian Wierzbicki, Anna Fryskowska, Michal Kedzierski, Michalina Wojtkowska, Paulina Delis. Method of radiometric quality assessment of NIR images acquired with a custom sensor mounted on an unmanned aerial vehicle. Journal of Applied Remote Sensing. 2018; 12 (1):015008.

Chicago/Turabian Style

Damian Wierzbicki; Anna Fryskowska; Michal Kedzierski; Michalina Wojtkowska; Paulina Delis. 2018. "Method of radiometric quality assessment of NIR images acquired with a custom sensor mounted on an unmanned aerial vehicle." Journal of Applied Remote Sensing 12, no. 1: 015008.

Journal article
Published: 18 August 2017 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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The archaeological heritage is non-renewable, and any invasive research or other actions leading to the intervention of mechanical or chemical into the ground lead to the destruction of the archaeological site in whole or in part. For this reason, modern archeology is looking for alternative methods of non-destructive and non-invasive methods of new objects identification. The concept of aerial archeology is relation between the presence of the archaeological site in the particular localization, and the phenomena that in the same place can be observed on the terrain surface form airborne platform. One of the most appreciated, moreover, extremely precise, methods of such measurements is airborne laser scanning. In research airborne laser scanning point cloud with a density of 5 points/sq. m was used. Additionally unmanned aerial vehicle imagery data was acquired. Test area is located in central Europe. The preliminary verification of potentially microstructures localization was the creation of digital terrain and surface models. These models gave an information about the differences in elevation, as well as regular shapes and sizes that can be related to the former settlement/sub-surface feature. The paper presents the results of the detection of potentially sub-surface microstructure fields in the forestry area.

ACS Style

A. Fryskowska; Michal Kedzierski; P. Walczykowski; Damian Wierzbicki; P. Delis; A. Lada. EFFECTIVE DETECTION OF SUB-SURFACE ARCHEOLOGICAL FEATURES FROM LASER SCANNING POINT CLOUDS AND IMAGERY DATA. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2017, XLII-2/W5, 245 -251.

AMA Style

A. Fryskowska, Michal Kedzierski, P. Walczykowski, Damian Wierzbicki, P. Delis, A. Lada. EFFECTIVE DETECTION OF SUB-SURFACE ARCHEOLOGICAL FEATURES FROM LASER SCANNING POINT CLOUDS AND IMAGERY DATA. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2017; XLII-2/W5 ():245-251.

Chicago/Turabian Style

A. Fryskowska; Michal Kedzierski; P. Walczykowski; Damian Wierzbicki; P. Delis; A. Lada. 2017. "EFFECTIVE DETECTION OF SUB-SURFACE ARCHEOLOGICAL FEATURES FROM LASER SCANNING POINT CLOUDS AND IMAGERY DATA." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W5, no. : 245-251.

Journal article
Published: 18 August 2017 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Terrestrial Laser Scanning is currently one of the most common techniques for modelling and documenting structures of cultural heritage. However, only geometric information on its own, without the addition of imagery data is insufficient when formulating a precise statement about the status of studies structure, for feature extraction or indicating the sites to be restored. Therefore, the Authors propose the integration of spatial data from terrestrial laser scanning with imaging data from low-cost cameras. The use of images from low-cost cameras makes it possible to limit the costs needed to complete such a study, and thus, increasing the possibility of intensifying the frequency of photographing and monitoring of the given structure. As a result, the analysed cultural heritage structures can be monitored more closely and in more detail, meaning that the technical documentation concerning this structure is also more precise. To supplement the laser scanning information, the Authors propose using both images taken both in the near-infrared range and in the visible range. This choice is motivated by the fact that not all important features of historical structures are always visible RGB, but they can be identified in NIR imagery, which, with the additional merging with a three-dimensional point cloud, gives full spatial information about the cultural heritage structure in question. The Authors proposed an algorithm that automates the process of integrating NIR images with a point cloud using parameters, which had been calculated during the transformation of RGB images. A number of conditions affecting the accuracy of the texturing had been studies, in particular, the impact of the geometry of the distribution of adjustment points and their amount on the accuracy of the integration process, the correlation between the intensity value and the error on specific points using images in different ranges of the electromagnetic spectrum and the selection of the optimal method of transforming the acquired imagery. As a result of the research, an innovative solution was achieved, giving high accuracy results and taking into account a number of factors important in the creation of the documentation of historical structures. In addition, thanks to the designed algorithm, the final result can be obtained in a very short time at a high level of automation, in relation to similar types of studies, meaning that it would be possible to obtain a significant data set for further analyses and more detailed monitoring of the state of the historical structures.

ACS Style

Michal Kedzierski; P. Walczykowski; M. Wojtkowska; A. Fryskowska. INTEGRATION OF POINT CLOUDS AND IMAGES ACQUIRED FROM A LOW-COST NIR CAMERA SENSOR FOR CULTURAL HERITAGE PURPOSES. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2017, XLII-2/W5, 407 -414.

AMA Style

Michal Kedzierski, P. Walczykowski, M. Wojtkowska, A. Fryskowska. INTEGRATION OF POINT CLOUDS AND IMAGES ACQUIRED FROM A LOW-COST NIR CAMERA SENSOR FOR CULTURAL HERITAGE PURPOSES. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2017; XLII-2/W5 ():407-414.

Chicago/Turabian Style

Michal Kedzierski; P. Walczykowski; M. Wojtkowska; A. Fryskowska. 2017. "INTEGRATION OF POINT CLOUDS AND IMAGES ACQUIRED FROM A LOW-COST NIR CAMERA SENSOR FOR CULTURAL HERITAGE PURPOSES." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W5, no. : 407-414.

Journal article
Published: 21 June 2017 in Remote Sensing
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The standard ratio of spatial resolution between bands for high resolution satellites is 1:4, which is typical when combining images obtained from the same sensor. However, the cost of simultaneously purchasing a set of panchromatic and multispectral images is still relatively high. There is therefore a need to develop methods of data fusion of very high resolution panchromatic imagery with low-cost multispectral data (e.g., Landsat). Combining high resolution images with low resolution images broadens the scope of use of satellite data, however, it is also accompanied by the problem of a large ratio between spatial resolutions, which results in large spectral distortions in the merged images. The authors propose a modification of the panchromatic image in such a way that it includes the spectral and spatial information from both the panchromatic and multispectral images to improve the quality of spectral data integration. This fusion is done based on a weighted average. The weight is determined using a coefficient, which determines the ratio of the amount of information contained in the corresponding pixels of the integrated images. The effectiveness of the author’s algorithm had been tested for six of the most popular fusion methods. The proposed methodology is ideal mainly for statistical and numerical methods, especially Principal Component Analysis and Gram-Schmidt. The author’s algorithm makes it possible to lower the root mean square error by up to 20% for the Principal Component Analysis. The spectral quality was also increased, especially for the spectral bands extending beyond the panchromatic image, where the correlation rose by 18% for the Gram-Schmidt orthogonalization.

ACS Style

Aleksandra Grochala; Michal Kedzierski. A Method of Panchromatic Image Modification for Satellite Imagery Data Fusion. Remote Sensing 2017, 9, 639 .

AMA Style

Aleksandra Grochala, Michal Kedzierski. A Method of Panchromatic Image Modification for Satellite Imagery Data Fusion. Remote Sensing. 2017; 9 (6):639.

Chicago/Turabian Style

Aleksandra Grochala; Michal Kedzierski. 2017. "A Method of Panchromatic Image Modification for Satellite Imagery Data Fusion." Remote Sensing 9, no. 6: 639.

Proceedings article
Published: 01 June 2017 in 2017 Baltic Geodetic Congress (BGC Geomatics)
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Technological advances and an increased demand for reconnaissance images acquired in different spectral ranges of the electromagnetic spectrum for Imagery Intelligence have contributed to the construction of a reconnaissance pod system DB-110. The main aim of this article was a comparison of pictures acquired in the visible, near-infrared and mid-infrared ranges. The analyses was carried out by comparing the general construction and statistical data of the images, and their relevance for the evaluation of the terrain, the characteristics of individual objects, conducting various types of spatial analyses and the usefulness of the process of image interpretation. It was found that the images from the electrooptical scanner are a better source for the image interpretation process in identification terms. The thermal images enable the detection of objects that are not detectable in the images from the visible and near-infrared spectral ranges of the electromagnetic spectrum.

ACS Style

Michal Kedzierski; Damian Wierzbicki; Paulina Delis; Marcin Walkowiak. Analysis of Reconnaissance Imagery Acquired in Different Spectral Ranges of the Electromagnetic Spectrum. 2017 Baltic Geodetic Congress (BGC Geomatics) 2017, 59 -64.

AMA Style

Michal Kedzierski, Damian Wierzbicki, Paulina Delis, Marcin Walkowiak. Analysis of Reconnaissance Imagery Acquired in Different Spectral Ranges of the Electromagnetic Spectrum. 2017 Baltic Geodetic Congress (BGC Geomatics). 2017; ():59-64.

Chicago/Turabian Style

Michal Kedzierski; Damian Wierzbicki; Paulina Delis; Marcin Walkowiak. 2017. "Analysis of Reconnaissance Imagery Acquired in Different Spectral Ranges of the Electromagnetic Spectrum." 2017 Baltic Geodetic Congress (BGC Geomatics) , no. : 59-64.

Proceedings article
Published: 01 June 2017 in 2017 Baltic Geodetic Congress (BGC Geomatics)
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Wave-length dependant imaging errors of the optical components and different characteristics in colour channels yield various geometric errors of the RGB image. Chromatic aberration has a very large direct impact on the quality of a digital image correlation and indirect on the geometry of the image. This is particularly important when recording black and white images on the RGB array. It is also particularly important for images taken with low-cost cameras mounted on UAVs. In the paper a novel method of detection of chromatic aberration with the use of wavelet analysis has been proposed. For the purpose of these studies two types of tests were designed and developed. A non-metric low-cost digital camera had been used. The proposed method of detection and correction of chromatic aberration can be applied in photogrammetric and remote sensing products generated on the basis of images from cameras mounted on UAVs

ACS Style

Anna Fryskowska; Michal Kedzierski; Michalina Wojtkowska; Aleksandra Grochala. A Novel Method of Chromatic Aberration Detection and Correction Using Wavelet Analysis. 2017 Baltic Geodetic Congress (BGC Geomatics) 2017, 18 -24.

AMA Style

Anna Fryskowska, Michal Kedzierski, Michalina Wojtkowska, Aleksandra Grochala. A Novel Method of Chromatic Aberration Detection and Correction Using Wavelet Analysis. 2017 Baltic Geodetic Congress (BGC Geomatics). 2017; ():18-24.

Chicago/Turabian Style

Anna Fryskowska; Michal Kedzierski; Michalina Wojtkowska; Aleksandra Grochala. 2017. "A Novel Method of Chromatic Aberration Detection and Correction Using Wavelet Analysis." 2017 Baltic Geodetic Congress (BGC Geomatics) , no. : 18-24.

Journal article
Published: 12 May 2017 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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These studies have been conductedusing non-metric digital camera and dense image matching algorithms, as non-contact methods of creating monuments documentation.In order toprocess the imagery, few open-source software and algorithms of generating adense point cloud from images have been executed. In the research, the OSM Bundler, VisualSFM software, and web application ARC3D were used. Images obtained for each of the investigated objects were processed using those applications, and then dense point clouds and textured 3D models were created. As a result of post-processing, obtained models were filtered and scaled.The research showedthat even using the open-source software it is possible toobtain accurate 3D models of structures (with an accuracy of a few centimeters), but for the purpose of documentation and conservation of cultural and historical heritage, such accuracy can be insufficient.

ACS Style

M. Zacharek; P. Delis; Michal Kedzierski; A. Fryskowska. GENERATING ACCURATE 3D MODELS OF ARCHITECTURAL HERITAGE STRUCTURES USING LOW-COST CAMERA AND OPEN SOURCE ALGORITHMS. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2017, XLII-5/W1, 99 -104.

AMA Style

M. Zacharek, P. Delis, Michal Kedzierski, A. Fryskowska. GENERATING ACCURATE 3D MODELS OF ARCHITECTURAL HERITAGE STRUCTURES USING LOW-COST CAMERA AND OPEN SOURCE ALGORITHMS. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2017; XLII-5/W1 ():99-104.

Chicago/Turabian Style

M. Zacharek; P. Delis; Michal Kedzierski; A. Fryskowska. 2017. "GENERATING ACCURATE 3D MODELS OF ARCHITECTURAL HERITAGE STRUCTURES USING LOW-COST CAMERA AND OPEN SOURCE ALGORITHMS." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-5/W1, no. : 99-104.

Conference paper
Published: 20 April 2017 in Conference on Reconnaissance and Electronic Warfare Systems
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Remote acquisition of information about phenomena and objects from an imagery is the main objective of remote sensing. The ability to realize aims of image intelligence depends on the quality of acquired remote sensing data. The imagery intelligence can be carried out from different altitudes- from satellite level to terrestrial platforms. In this article, authors are focused on chosen aspects of imagery intelligence from low altitudes. Unfortunately the term low altitudes is not precise defined, therefore, for the purpose of this article is assumed that low altitudes, are altitudes in which operate the mini unmanned aerial vehicles (mini UAVs).The quality of imagery acquired determines the level of analysis that can be performed. The imagery quality depends on many factors, such as platforms on which the sensor is mounted, imaging sensors, height from which the data are acquired and object that is investigated. The article will also present the methods for assessing the quality of imagery in terms of detection, identification, description and technical analysis of investigated objects, as well as in terms of the accuracy of their location in the images (targeting). © (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

ACS Style

P. Walczykowski; M. Kedzierski. Imagery intelligence from low altitudes: chosen aspects. Conference on Reconnaissance and Electronic Warfare Systems 2017, 10418, 104180 .

AMA Style

P. Walczykowski, M. Kedzierski. Imagery intelligence from low altitudes: chosen aspects. Conference on Reconnaissance and Electronic Warfare Systems. 2017; 10418 ():104180.

Chicago/Turabian Style

P. Walczykowski; M. Kedzierski. 2017. "Imagery intelligence from low altitudes: chosen aspects." Conference on Reconnaissance and Electronic Warfare Systems 10418, no. : 104180.

Journal article
Published: 01 October 2016 in Measurement
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Low-altitude photogrammetry studies have been more and more popular in the mapping of small areas (up to 10 thousand hectares). UAV flights can be consider as an attractive low-cost alternative solution for the photogrammetric studies. However, in this type of platforms images are frequently captured by digital compact cameras. Despite high resolution, the images taken with these cameras have a relatively low radiometric quality. While photogrammetric software for processing images obtained via sensors mounted on UAVs and the possible applications of the GPS RTK system for determining projection centres are constantly developing, the majority of studies nowadays still require digital aerial triangulation based on transferring and measuring tie points on subsequent images of the same surface fragment. A method for improving the quality of low-altitude image data is presented in this article. In order to improve the image radiometry, filtration in frequency domain was applied. This solution made it possible to enhance the reflection from objects in the images andat the same time reduce the impact of poor lighting on local contrast. The proposed method comprises two variants of radiometric correction, each of thesedepending on the quality of the pictures. The effectiveness of the method has been proven by adjusting three image blocks with different levels of radiometric quality before and after filtration, as well as a comparative analysis of the aerial triangulation results.

ACS Style

Michal Kedzierski; Damian Wierzbicki. Methodology of improvement of radiometric quality of images acquired from low altitudes. Measurement 2016, 92, 70 -78.

AMA Style

Michal Kedzierski, Damian Wierzbicki. Methodology of improvement of radiometric quality of images acquired from low altitudes. Measurement. 2016; 92 ():70-78.

Chicago/Turabian Style

Michal Kedzierski; Damian Wierzbicki. 2016. "Methodology of improvement of radiometric quality of images acquired from low altitudes." Measurement 92, no. : 70-78.