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Обнаружение аномалий на гиперспектральных изображениях

Обнаружение аномалий на гиперспектральных изображениях

Abstract

В статье предлагается несколько новых алгоритмов обнаружения аномалий на гиперспектральных изображениях, использующих различные способы определения факта «аномальности». Производится сравнение предлагаемых алгоритмов с широко используемым RXD-алгоритмом и его модификациями, на ряде примеров продемонстрированы преимущества предложенных решений.

In this paper authors offered several algorithms for anomaly detection on hyperspectral images. Algorithms used different ideas to describe anomalies. A comparison between offered in article algorithms and RXD-detector was provided. An advances of proposed solutions were overviewed.

Keywords

ГИПЕРСПЕКТРАЛЬНЫЕ ИЗОБРАЖЕНИЯ, ОБНАРУЖЕНИЕ АНОМАЛИЙ, СПЕКТРАЛЬНОЕ РАССОГЛАСОВАНИЕ, RX-ДЕТЕКТОР АНОМАЛИЙ

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
gold