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1. SAR IMAGES RETRIEVAL BASED ON SEMANTIC CLASSIFICATION AND
REGION-BASED SIMILARITY MEASURE FOR EARTH OBSERVATION
ABSTRACT
Based on the semantic categorization and regionbasedsimilarity measure, a novel
synthetic aperture radar (SAR)image retrieval method is proposed in this paper, which is
inspiredby the existing content-based image retrieval (CBIR) techniquesand is oriented toward
the Earth observation (EO). First, dueto the large sizes of SAR images, new method semantically
classifiesthe land covers in the patch level rather than the pixellevel by the classic
semisupervised learning (SSL), which couldreduce the workload of selecting the representative
image patchand decrease the searching space in the similarity calculationcomponent.
Furthermore, to overcome the inevitable classificationerror, our method provides an error
recovery scheme, preventingthe errors produced in categorization to contaminate the retrieval
results. Third, the similarity between two patches is calculated bythe improved integrated region
matching (IIRM) measure basedon the region-based similarity measure, which fails to meet the
expectation in SAR images. The proposed method can be embeddedinto any EO mining systems
to help them complete the EOmissions. After comparing the method presented in this paper to
others, it is evident that our method performs more effectivelythan others from the CBIR aspect.