Електроний каталог - Науково-технічна бібліотека ім М.Є. Жуковського ХАІ

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Proskura G., Rubel O., Kryvenko S., Lukin V.
On classifier performance for remote sensing images compressed by different coders

Вид документа: Стаття періодики
Автор: Proskura G., Rubel O., Kryvenko S., Lukin V. Вид автора: персона
Мова: Англійська Обсяг: P. 67-77
УДК: 528.852.1

Аннотацiя:

Remote sensing data are widely used in numerous applications. A conventional task solved using remote sensing images is their classification. The classification maps are commonly produced by some pre-trained classifiers applied either to uncompressed or compressed images where lossy compression is often needed and employed in practice due to the necessity to reduce data volume at stages of image transfer and storage. Then, the classification accuracy depends on the characteristics of an image, a classifier, and a coder used. The main subject of this paper is the factors that determine classification accuracy. One of them is compressed image quality. We fix the quality of compressed image quality characterized by the peak signal-to-noise ratio for several coders and rely on the same training approach. Our goal is twofold. First, we would like to consider classification accuracy for two approaches to classifier training: based on undistorted data and images with simulated distortions. Second, our desire

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Є складовою частиною документа: Авіаційно-космічна техніка і технологія

Відомості щодо головного

Назва головного документа:

Авіаційно-космічна техніка і технологія

Дата видання головного документа:

2023

Номер частини головного документа:

Загальна інформація

Бібліографія:

Bibliogr.: 26 names

Теми документа: