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.
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.