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.

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.

Sustainable development and the creation of smart, green cities requires cooperation in many scientific fields, including those related to ecology, mobility, or sustainable management, among others. Environmental protection is a particularly important element here. Atmospheric pollution, due to air movements, spreads over very large areas; therefore, air quality monitoring is crucial to ensure protection from harmful substances. One of the most severe sources of air pollution, accounting for as much as approximately 25% of total annual emissions within the EU, is road transport. Therefore, the European Union has set an ambitious target to reduce total emissions to 55% for cars and 50% for vans by 2030. In recent years, unmanned aerial vehicles (UAVs) have become increasingly popular in many scientific fields, including environmental protection and photogrammetry. The use of UAVs to identify harmful pollutants allows them to gain an advantage over conventional detection methods, due to the possibility of remote (therefore safe for humans), faster, and area-based measurements. Given the ever-expanding scale of the use of this technology, this paper presents the possibilities of using UAVs to identify and visualize (map) pollution. The examples presented in the foreign literature, as well as our own research, in imaging the altitude distribution of air pollutants; gaseous pollutants: C₆H₆, HCHO, SO₂; and particulate matter: PM₁, PM_2.5, PM₁₀ demonstrate the validity of such measures. This research was carried out in the area of one of Poland’s key A4 highways. The maps obtained allow for an area-wise and altitude-wise presentation of one of the significant air pollutants in the EU. In addition, they can be a valuable source of information for the implementation of future projects and the improvement of road infrastructure, thus contributing to the reduction of air pollution and the creation of so-called “green cities”.

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.

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.

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.