The purpose of the research is to develop a methodology for testing the camouflage fabrics using hyperspectral data. The fabrics used in the research have been overexploited and have been subjected to a variety of chemicals. The research is aimed at answering how and how often the masking fabrics should be examined, what processes eliminate the fabric from their camouflage functions and at what time the regular use disqualifies a given fabric from further use. The research was conducted in the laboratory conditions. For the study, VNIR hyperspectral camera was used, produced by Headwall. In the presented study, selected camouflage fabric samples were used. They were exposed to long-lasting effects of various atmospheric conditions, such as sunrays, rain, snow, etc. The time of exposure of textiles and the type of conditions was monitored. In addition, samples were used that were subjected to the action of various chemicals, e.g. chlorine. Chemicals applied in the research are widely used for cleaning fabrics. The influence of chemicals other than those commonly used in textile maintenance has not been verified. Based on the hyperspectral imagery data, the spectral reflectance characteristics of processed samples were retrieved. Next, developed spectral indices were applied in order to determine the degree of ageing. Additionally, hyperspectral images of used material samples have been subjected to a classification process using spectral reflectance characteristics of brand new materials. This allowed to determine the divergence between the samples and determining the degree of destruction of samples, depending on the ageing processes used. Trends were set, and the conditions and activities were eliminated from the use of fabrics. Finally, based on the obtained results, methodologies for testing the camouflage fabrics and determining their usefulness were developed.
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.
Remote sensing is one of the most dynamically developing fields of science that can be applicable in security and biometrics. Application of various biometric systems has been increased in past decades due to growing demands of security. There are a lot of human characteristics that might be used in a biometric system to identify an individual, i.e., hand palm veins, fingerprints, iris recognition, etc. Hand geometry biometric is based on the shape of hands. Hand palm veins based biometry that uses the vascular system patterns is one of the most complex and can be utilized for personal identification. Therefore, the hand palm veins based biometry includes some of the most useful technologies for person identification. This paper examines the utility of hyperspectral imagery for personal identification based on the fusion of hand geometry and hand palms veins biometrics. The hyperspectral data are used to develop the methodology of fast imagery post-processing and data matching, which could be used to develop a low-cost hand biometric recognition system intended to make a highly efficient identification and to have a fast response and easy usage. The research was conducted using a hyperspectral pushbroom sensor in controlled laboratory conditions. The hand vein detection process is based on a camera that takes a picture of the subject’s veins under a radiation source. The application of hyperspectral data can be time-consuming. Therefore biometrics systems should use faster solutions. Experiment with a hyperspectral camera allowed to select the best wavelength in which acquisition of palm veins and hand geometry was optimal. The developed methodology will be next used to build a low-cost system with one panchromatic frame camera and adequate interferometric filter, that would facilitate data acquisition, and identification process.
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).
Monitoring of water environment and ecosystem, detecting water contaminants and understanding water quality parameters are most important tasks in water management and protection of whole aquatic environment. Detection of biological contaminants play a very important role in preserving human health and water management. To obtain accurate and precise results of determination of the level of biological contamination and to distinguish its type it is necessary to determine precisely spectral reflectance coefficients of several water biological pollutants with inter alia spectroradiometer. This paper presents a methodology and preliminary results of acquisition of spectral reflectance coefficients with different reference panels (e.g. with 5%, 20%, 50%, 80% and 96% of reflectivity) of several biological pollutants. The authors’ main task was to measure spectral reflectance coefficients of different biological water pollutants with several reference panels and to select optimal reference standard, which would allow for distinguish different types of several biological contaminants. Moreover it was necessary to indicate the spectral range in which it is possible to discriminate investigated samples of biological contaminants. By conducting many series of measurements of several samples of different types of biological pollutants, authors had concluded how the reflectivity of reference panel influences the accuracy of acquisition of spectral reflectance coefficients. This research was crucial in order to be able to distinguish several types of biological pollutants and to determine the useful spectral range for detection of different kinds of biological contaminants with multispectral and hyperspectral imagery.