Аннотация (русский):
The main objective of this book is to acquaint the reader with the main modern problems of the multisensor data analysis and opportunities of the hyperspectral shooting being carried out in the wide range of wavelengths from ultraviolet to the infrared range, visualization of the fast combustion processes of flame propagation and flame acceleration, the limit phenomena at flame ignition and propagation. The book can be useful to students of the high courses and scientists dealing with problems of optical spectroscopy, vizualisation, digital recognizing images and gaseous combustion. The main goal of this book is to bring to the attention of the reader the main modern problems of multisensory data analysis and the possibilities of hyperspectral imaging, carried out in a broad wave-length range from ultraviolet to infrared by methods of visualizing fast combustion processes, propagation and flames acceleration, and limiting phenomena during ignition and flame propagation. The book can be useful for students of higher courses and experimental scientists dealing with problems of optical spectroscopy, visualization, pattern recognition and gas combustion.

Ключевые слова:
Remote measurements, optoelectronic methods, multisensor data analysis, hyper spectral shooting, ramjet engine, Catalytic Stabilization

Methods and means of remote sensing in the optical range are considered. The advantages of using multispectral multisensor sensing, which significantly increase the efficiency of remote analysis of both images and combustion and explosion processes, are demonstrated. Keywords: video camera, hyperspectrometer, thermal imager, UV sensor, remote sensing, image, alignment Since the seventies of the last century, remote sensing methods in the optical range have undergone significant changes. In particular, such effective means of remote sensing as spectrozonal, multispectral and hyperspectral survey have appeared. Accordingly, adequate processing methods were developed, taking into account the spectral features of the interaction of waves of various ranges with the material of the probed objects and their morphological structure. We will point out here only the methods of spectral synthesis, neural network algorithms and the method of principal components. At the same time, all these methods related only to the processing of data obtained by one sensor. However, an approach based on the processing of multispectral data obtained by different sensors when they simultaneously shoot the same scene is of certain interest. It is obvious that sensors can have different spatial resolution, different angular field of view, sensitivity, etc. This approach is a development of the principle of multispectral sensing, since the creation of a single device that would cover the entire optical range from ultraviolet to far infrared and would have high values of spatial and spectral resolution seems to be very problematic. In this regard, the purpose of this Chapter is to demonstrate the capabilities of multisensory imaging aimed at improving the efficiency of vision systems through the joint analysis of images obtained in different wavelength ranges. In particular, it is shown that the joint analysis of data from different sensors can achieve a significant synergistic effect and create a basis for the so-called extended vision system [1]. Initially, used in aerial and space imaging of the Earth, as well as in laboratory experiments, panchromatic imaging made it possible to obtain images with a high spatial resolution (due to the higher sensitivity of black-and-white photographic film) (Fig. 1), but it did not provide the necessary distinguishability objects with color (spectral) differences. In point of fact, the image contrast was formed by adding spectral contrasts in quadrature (according to "power") without taking into account their phase relationships during panchromatic, since different spectral contrasts could even be opposite and, when added, compensate each other. Of course, full compensation could not be due to their different weight ratios, but the result was a decrease in the total contrast. So, it was necessary to "sweep" the radiation received by the sensor along the wavelength. The first intuitive solution to this problem was the use of first color and then multispectral photography, in which the photographic film was sensitized to different spectral zones. Finally, the so-called multi-zone method was developed, in which a camera was used with several lenses equipped with filters with different spectral transmission bands. Fig. 1. City of Los Angeles (USA). The picture was taken by domestic optoelectronic equipment with a resolution of 1 m from an altitude of 475 km in the panchromatic range from the "Resurs-P" spacecraft in June 2013. The solar radiation reflected from the sensing object passed through such a filter fell on a highly sensitive black-and-white film, forming a spectrozonal image on it. The black-and-white negatives (positives) obtained then were illuminated by light sources with real or conventional colors with the help of special devices and projected onto a common screen. At the same time, it was possible to interactively project on the screen both the negatives and the positives of the images in order to produce their in-phase summation, as well as to give each of them its own weight. This procedure is called "image synthesis". The most famous of such space sensors is the MKF-6 multispectral space camera, which has successfully passed field tests on the "Soyuz-22" spacecraft. Even the first experiments with multispectral images have shown their high efficiency in recognizing objects in images (Fig. 2). Fig. 2. Synthesized image in conditional colors of the territory of the "Mir" diamond quarry, obtained by the MKF-6 camera A further development of multispectral imaging was the appearance of opto-electronic multispectral MSS scanners on the ERTS-1 satellites AS (Landsat), "Meteor", SPOT, etc. (see Fig. 3). As the number of spectral channels of sensors increased and the methods of their processing were improved, the information content of the data obtained with their help enhanced. In particular, it was found that the maximum contrast of the probed objects occurs in the images corresponding to the first main component of the original multispectral data. Fig. 3. Synthesized image of the territory of Northern Ukraine in conditional colors, obtained on the basis of imagery with a multispectral MSS scanner from the ERTS-1 AS (Landsat). Spectrometers used on board aircraft and satellites, which possessed up to several hundred spectral channels (for example, "Spectrum-256"), provided only path survey and did not allow obtaining spectral images of the terrain and, therefore, could not compete with multispectral scanners. The situation has changed dramatically with the advent of laboratory and onboard hyperspectral sensors, which provide simultaneous acquisition of several hundred spectral images recorded on a photodetector matrix. One of the first representatives of the line of hyperspectrometers developed at «SPC «Reagent» CJSC [2] is shown in Fig. 4. Its main technical characteristics are shown below. Spectral range, nm 400 – 1000 Spectral resolution, nm 1 – 10 Angular spatial resolution, rad 1· 10-3 Number of independent spectral channels 224 Signal-to-noise ratio more 100 To demonstrate the possibilities of using such sensors, we present a hyperspectral image of the fire zone (Fig. 5), obtained with a VID-IK 3 hyperspectrometer (Fig. 5). Fig. 4. Photo of a VID-IK3 hyperspectrometer. Fig. 5. RGB image of a fire obtained by a hyperspectrometer: 1-area affected by fire; 2-area, not affected by fire. At the bottom of Fig. 5, the emission spectra corresponding to the pixels highlighted on it (points 1 and 2) are shown. For these spectra, the absorption of solar radiation was not taken into account, therefore, the most characteristic peaks associated with the absorption of solar radiation in the atmosphere are clearly visible on them. Spectrum 1 clearly shows a peak in the region of 450-700 nm associated with chlorophyll. In fig. 6, an image obtained based on the first main component of this hyperspectral image is shown. It is interesting because the boundaries of objects are more clearly visible on it than on the RGB image due to the higher contrast of the depicted objects. Fig. 6. The first main component of the hyperspectral image. Thus, the hyperspectral image obtained using the first principal component algorithm has the highest possible contrast achieved by a single sensor. A detailed description of the line of hyperspectral sensors created at "SPC" "Reagent" CJSC, some of which were used to obtain the results described in the following Chapters, is given in Chapter 2. Naturally, the question of using a hyperspectrometer to study combustion and explosion processes in laboratory conditions has arisen. Thus, in [3, 4], the prospects for such a study were demonstrated and experimental results were presented that cannot be obtained by traditional emission optical spectroscopy. For this, a laboratory hyperspectrometer was created for remote sensing of reflected, scattered and emitted light from a distance of 3 m (Fig. 7). Fig. 7. The laboratory hyperspectrometer It was shown that the created hyperspectrometer can be effectively used to control and study combustion and explosion processes. The possibility of studying the processes occurring during combustion and explosion simultaneously in a wide range of wavelengths turned out to be especially interesting. In addition, the hyperspectrometer provides measurement of the time dependence of the glow that occurs during combustion and explosion (see Fig. 8). Fig. 8. Spectra of explosion radiation versus time. As mentioned in the introduction, digital compact-size cameras are capable of high-speed video recording at up to 1200 frames per second at reduced frame sizes. Such measurements make it possible to visualize the combustion and explosion processes (in particular, to register the movement of the flame front in time), but do not make it possible to determine the chemical composition of the products. Therefore, it turned out to be interesting to combine the simultaneous use of high-speed color [5] and hyperspectral photography for studying combustion and explosion processes. Another promising optoelectronic sensor is an ultraviolet sensor in the UV-C range (UV-C sensor). Several versions of this device have been developed at «SPC «Reagent» CJSC. In fig. 9 the "Corona" sensor is shown [6]. A more detailed description and operation of the UV sensor is given in Chapter 2. This sensor detects radiation in the UV range of 250-280 nm. The UV range is interesting because solar UV radiation is absorbed by the ozone layer and the "Corona" sensor can register radiation in sunlight. To illustrate the possibility of detailing such catastrophic phenomena as fire when using various sensors: the UV-device "Corona", a hyperspectrometer and a thermal imager, the fire centers were recorded. Fig. 9. "Corona" UV-C sensor. So, in fig. 10 and 11 a video image of the area with a fire in the background is shown. The "Corona" sensor is visible in the foreground. Fig. 10. Video image of a scene with a fire that does not fall within the visible area of the "Corona" device. Fig. 11. Video image of a scene with a fire falling into the viewing field of the "Corona" device. Fig. 12. The image obtained by the "Corona" device for the case when the fire zone did not get into its viewing field (see Fig. 10). Fig. 13. The image obtained by the "Corona" device for the case when the fire zone get into its field of viewing (see Fig. 11). Analysis of the images in Fig. 10-13 allows us to conclude that the joint use of video and UV-C data makes it possible to accurately georeference the fire centers and study its structure. In addition, the UV image allows you to identify several local fire sources and determine its front. So, video filming gives a general view of the scene, while the UV image allows you to identify the fires inside the smoke plume. The next step was to study the possibilities of combining video and thermal photography when they probe the same scene with a fire (Fig. 14 and 15, respectively). As in the previous case, Fig. 14 gives an overview description of the fire pattern and reveals the geometry of the smoke plume well. However, the thermal imaging image (Fig. 15) demonstrates the internal structure of the fire and allows you to highlight the open fire zone and, thus, show latent information hidden from the eye. Joint consideration of both images provides an informational synergistic effect that cannot be obtained separately by each sensor. Fig. 14. Video image of the fire zone. Fig. 15. Image of the fire zone, obtained with a thermal imager in the range of 8-14 microns. Conclusions for Chapter 1 This Chapter discusses a method for multispectral analysis of remote sensing data using various sensors and assesses the capabilities of multisensor imagery carried out in a wide range of wavelengths from ultraviolet to infrared. Various combinations of data make it possible to reveal the detailed structure of the analyzed scene, study the spectral composition of the radiation of objects and distinguish point sources by it. The prospects of using various sensors for studying combustion and explosion processes are shown. The results of using different sensors will be demonstrated in subsequent Chapters.

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