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Ice reconnaissance data processing under low quality source images

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A practically effective solution to the problem of automated processing of ice reconnaissance data in high latitudes is proposed. The intermediate result of ice reconnaissance is huge aerial survey data set consisting of images of low quality; this is a consequence of the difficult conditions of aerial survey in high latitudes. The goal of the study is to create a high-level method that can either efficiently process this pre-collected data set or perform real-time processing of similar images while ensuring high reliability in solving the problem of recognizing ice class distribution on the water surface with minimal computing resources. In particular, the problem of automatic classification of ice-floe size distribution (FSD) type for a three-class model based on aerial survey data is solved. The practically important case of low-quality images is considered, a common situation for the meteorological conditions of the Far North. The proposed approach is based on the use of machine learning methods, in particular on the well-known multi-class SVM (Support Vector Machine), which is extremely undemanding to computing resources and therefore can be implemented even by the onboard computer of an ice reconnaissance UAV. From the input images of low quality some numerical characteristics of the image are calculated which informatively characterize the image. These characteristics (features) are invariant to scaling, rotation and illumination as well as have a much smaller dimensionality than the original image. The main idea underlying the proposed method is to form an original set of features which are implemented in the original feature space. These features characterize large fragments of the analyzed image and are “stable”, in contrast to the features that characterize small details. A new method of FSD type classification based on the processing of aerial survey data by using machine learning methods, which is sufficiently effective for processing low-quality images, has been proposed. Also, the original feature space for classification was proposed which ensured high practical efficiency of this method. The method has shown high efficiency when it is tested on a data set composed of low-quality real images (high blurriness, vagueness, presence of meteorological noises). The developed algorithm can be used for express analysis of ice reconnaissance data, including an ice reconnaissance UAV on-board software component.

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