Similarity Measures of Satellite Images Using an Adaptive Feature Contrast Model

Abstract

Similarity measurement is one of key operations to retrieve “desired” images from an image database. As a famous psychological similarity measure approach, the Feature Contrast (FC) model is defined as a linear combination of both common and distinct features. In this paper, an adaptive feature contrast (AdaFC) model is proposed to measure similarity between satellite images for image retrieval. In the AdaFC, an adaptive function is used to model a variable role of distinct features in the similarity measurement. Specifically, given some distinct features in a satellite image, e.g., a COAST image, they might play a significant role when the image is compared with an image including different semantics, e.g., a SEA image, and might be trivial when it is compared with a third image including same semantics, e.g., another COAST image. Experimental results on satellite images show that the proposed model can consistently improve similarity retrieval effectiveness of satellite images including multiple geo-objects, for example COAST images.