AUTOMATICALLY DETECTING TEXTUAL CONTENT IN HIGH-RESOLUTION IMAGES

• Autor(a) principal: Basso, Dayara [UNESP]
• Data de Publicação: 2021
• Outros Autores: Colnago, Marilaine [UNESP], Azevedo, Samara, Negri, Rogério G. [UNESP], Casaca, Wallace [UNESP]
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1109/IGARSS47720.2021.9553189; http://hdl.handle.net/11449/223593
• topic: Remote sensing images; Text detection

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.