AUTOMATICALLY DETECTING TEXTUAL CONTENT IN HIGH-RESOLUTION IMAGES
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Data
2021-01-01
Autores
Basso, Dayara [UNESP]
Colnago, Marilaine [UNESP]
Azevedo, Samara
Negri, Rogério G. [UNESP]
Casaca, Wallace [UNESP]
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Resumo
Classifying targets in satellite images is a nontrivial task which requires dealing with a large number of undesirable elements such as clouds, building shadows and other unexpected objects. Among these, a commonly found element refers to artificially inserted post-processing objects like textual content, as the added text usually takes the form of watermarks, sensor specifications, street and place location names, etc. Manually selecting text segments is tedious, time-consuming, and requires the familiarity with image editing tools to precisely delineate these writing areas. Therefore, in this paper, a new automatic approach for detecting textual elements in satellite images is presented. Our approach combines cartoon-texture decomposition, thresholding-based rules, morphological operations, and connected component analysis into a fully automated and concise framework. Experiments on real satellite images and comparisons against well-established text detection methods demonstrate the high accuracy and low false-positive rate achieved by our approach when detecting textual content.
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Remote sensing images, Text detection
Como citar
International Geoscience and Remote Sensing Symposium (IGARSS), v. 2021-July, p. 4204-4207.