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.
ABSTRACT:One of the most important aspects of documenting cultural heritage sites is acquiring detailed and accurate data. A popular method of storing 3D information about historical structures is using 3D models. These models are built based on terrestrial or aerial laser scanning data. These methods are seldom used together. Historical buildings usually have a very complex design, therefore the input data, on the basis of which their 3D models are being built, must provide a high enough accuracy to model these complexities. The data processing methods used, as well as the modeling algorithms implemented, should be highly automated and universal. The main of the presented research was to analyze and compare various methods for extracting matching points. The article presents the results of combining data from ALS and TLS using reference points extracted both manually and automatically. Finally, the publication also includes an analysis of the accuracy of the data merging process.
ABSTRACT:Nowadays remote sensing plays a very important role in many different study fields, i.e. environmental studies, hydrology, mineralogy, ecosystem studies, etc. One of the key areas of remote sensing applications is water quality monitoring. Understanding and monitoring of the water quality parameters and detecting different water contaminants is an important issue in water management and protection of whole environment and especially the water ecosystem. There are many remote sensing methods to monitor water quality and detect water pollutants. One of the most widely used method for substance detection with remote sensing techniques is based on usage of spectral reflectance coefficients. They are usually acquired using discrete methods such as spectrometric measurements. These however can be very time consuming, therefore image-based methods are used more and more often. In order to work out the proper methodology of obtaining spectral reflectance coefficients from hyperspectral and multispectral images, it is necessary to verify the impact of cameras radiometric resolution on the accuracy of determination of them. This paper presents laboratory experiments that were conducted using two monochromatic XEVA video sensors (400-1700nm spectral data registration) with two different radiometric resolutions (12 and 14 bits). In view of determining spectral characteristics from images, the research team used set of interferometric filters. All data collected with multispectral digital video cameras were compared with spectral reflectance coefficients obtained with spectroradiometer. The objective of this research is to find the impact of cameras radiometric resolution on reflectance values in chosen wavelength. The main topic of this study is the analysis of accuracy of spectral coefficients from sensors with different radiometric resolution. By comparing values collected from images acquired with XEVA sensors and with the curves obtained with spectroradiometer it's possible to determine accuracy of imagebased spectral reflectance coefficients and decide which sensor will be more accurate to determine them for protection of water aquatic environment purpose.
The rapid and accurate detection and identification of water pollutants play an enormous role is preserving water ecosystems and protecting the surrounding environment. Such detection and identification are usually conducted in situ, using traditional measurement methods. These methods however are very time consuming and costly especially when conducting them on a large scale. A team of specialists from the Department of Remote Sensing and Photogrammetry from the Military University of Technology in Warsaw have been taking part in a project entitled "IRAMSWater-Innovative remote sensing system for the monitoring of pollutants in rivers, offshore waters and flooded areas" (PBS1/B9/8/2012) financed by the polish National Centre for Research and Development. Its main aim is the creation of a remote sensing system based on hyperspectral sensors which will enable the evaluation, detection and distribution of biological, physical and chemical pollutants in the examined waters in real time. These analyses will be conducted based on spectral characteristics of a wide selection of pollutants. In order to acquire these spectral characteristics in a precise manner from hyperspectral data, the research team was required to establish a precise methodology for obtaining these data. The most common approach is to acquire the imagery first, ensuring that each scene contains at least one reference panel with a well know spectral characteristic, and then transforming the imagery and calculating the spectral response curves during post-processing. The IRAMSWater team had proposed a method of extracting precise reflectance coefficients in quasi real time without the need for using a reference panel on the scene. This is done by precisely determining the camera exposure parameters in laboratory conditions. The article presents laboratory experiments conducted using two XEVA video sensors (400-1700 nm spectral data registration), which allowed the authors to form a methodology for acquiring spectral reflectance coefficients of objects within a scene without the need for a reference panel.
ABSTRACT: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.
ABSTRACT: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 threedimensional 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.
The article presents a set of initial results of a quality assessment study of 2 different types of sensors mounted on an unmanned aerial vehicle, carried out over an especially designed and constructed test field. The PIQuAT (Portable Imagery Quality Assessment Test Field) field had been designed especially for the purposes of determining the quality parameters of UAV sensors, especially in terms of the spatial, spectral and radiometric resolutions and chosen geometric aspects. The sensor used include a multispectral framing camera and a high-resolution RGB sensor. The flights were conducted from a number of altitudes ranging from 10 m to 200 m above the test field. Acquiring data at a number of different altitudes allowed the authors to evaluate the obtained results and check for possible linearity of the calculated quality assessment parameters. The radiometric properties of the sensors were evaluated from images of the grayscale target section of the PIQuAT field. The spectral resolution of the imagery was determined based on a number of test samples with known spectral reflectance curves. These reference spectral reflectance curves were then compared with spectral reflectance coefficients at the wavelengths registered by the miniMCA camera. Before conducting all of these experiments in field conditions, the interior orientation parameters were calculated for the MiniMCA and RGB sensor in laboratory conditions. These parameters include: the actual pixel size on the detector, distortion parameters, calibrated focal length (CFL) and the coordinates of the principal point of autocollimation (miniMCA - for each of the six channels separately.
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