Abstract
The subject matter of the article is methods of detecting spectral anomalies on images from remote sensing systems. The goal is to conduct a comparative analysis of methods for detecting spectral anomalies on images from remote sensing systems. The tasks are: analysis of the main methods of detecting spectral anomalies on images from remote sensing systems; processing of images from remote sensing systems using basic methods of detecting spectral anomalies; comparative assessment of the quality of methods for detecting spectral anomalies on images from remote monitoring systems. The methods used are: methods of digital image processing, mathematical apparatus of matrix theory, methods of mathematical modeling, methods of optimization theory, analytical and empirical methods of image comparison. The following results are obtained. The main methods of detecting spectral anomalies on images from remote sensing systems were analyzed. Processing of images from remote sensing systems using the basic methods of detecting spectral anomalies was carried out. A comparative assessment of the quality of methods for detecting spectral anomalies on images from remote monitoring systems was carried out. Conclusions. The spectral difference of the considered methods is revealed by the value of information indicators - Euclidean distance, Mahalanobis distance, brightness contrast, and Kullback-Leibler information divergence. Mathematical modeling of the considered methods of detecting spectral anomalies of images with a relatively “simple” and complicated background was carried out. It was established that when searching for a spectral anomaly on an image with a complicated background, the method based on the Kullback-Leibler divergence can be more effective than the other considered methods, but is not optimal. When determining several areas of the image with high divergence indicators, they should be additionally investigated using the specified methods in order to more accurately determine the position of the spectral anomaly.
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