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首页> 外文期刊>Geoscience and Remote Sensing Letters, IEEE >Image Mining Using Directional Spatial Constraints
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Image Mining Using Directional Spatial Constraints

机译：使用方向空间约束的图像挖掘

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摘要

Spatial information plays a fundamental role in building high-level content models for supporting analysts' interpretations and automating geospatial intelligence. We describe a framework for modeling directional spatial relationships among objects and using this information for contextual classification and retrieval. The proposed model first identifies image areas that have a high degree of satisfaction of a spatial relation with respect to several reference objects. Then, this information is incorporated into the Bayesian decision rule as spatial priors for contextual classification. The model also supports dynamic queries by using directional relationships as spatial constraints to enable object detection based on the properties of individual objects as well as their spatial relationships to other objects. Comparative experiments using high-resolution satellite imagery illustrate the flexibility and effectiveness of the proposed framework in image mining with significant improvements in both classification and retrieval performance.
机译：空间信息在建立高级内容模型以支持分析师的解释和自动化地理空间情报方面起着基本作用。我们描述了一个框架，用于建模对象之间的定向空间关系并将此信息用于上下文分类和检索。所提出的模型首先识别相对于多个参考对象具有高度空间关系满意度的图像区域。然后，该信息作为上下文分类的空间先验被合并到贝叶斯决策规则中。该模型还通过使用方向关系作为空间约束来支持动态查询，以基于单个对象的属性以及它们与其他对象的空间关系来进行对象检测。使用高分辨率卫星图像的比较实验说明了所提出的框架在图像挖掘中的灵活性和有效性，并且在分类和检索性能上都有显着改善。

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