SAR Images Retrieval Based on Semantic Classification and Region-Based Similarity Measure for Earth Observation
作者： Licheng Jiao , Xu Tang , Biao Hou , Shuang Wang
DOI: 10.1109/JSTARS.2015.2429137
关键词:
摘要: Based on the semantic categorization and region-based similarity measure, a novel synthetic aperture radar (SAR) image retrieval method is proposed in this paper, which inspired by existing content-based (CBIR) techniques oriented toward Earth observation (EO). First, due to large sizes of SAR images, new semantically classifies land covers patch level rather than pixel classic semisupervised learning (SSL), could reduce workload selecting representative decrease searching space calculation component. Furthermore, overcome inevitable classification error, our provides an error recovery scheme, preventing errors produced contaminate results. Third, between two patches calculated improved integrated region matching (IIRM) measure based fails meet expectation images. The can be embedded into any EO mining systems help them complete missions. After comparing presented paper others, it evident that performs more effectively others from CBIR aspect.
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