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This paper proposes a real-time semantics-driven infrared and visible image fusion framework (RSDFusion). A novel semantics-driven image fusion strategy is introduced in image fusion to maximize the retention of significant information of the source image in the fusion image. First, a semantically segmented image of the source image is obtained using a pre-trained semantic segmentation model. Second, masks of significant targets are obtained from the semantically segmented image, and these masks are used to separate the targets in the source and fusion images. Finally, the local semantic loss of the separation target is designed and combined with the overall structural similarity loss of the image to instruct the network to extract appropriate features to reconstruct the fusion image. Experimental results show that the RSDFusion proposed in this paper outperformed other comparative methods on both subjective and objective evaluation of public datasets and that the main target of the source image is better preserved in the fusion image. [ABSTRACT FROM AUTHOR]

This paper proposes a real-time semantics-driven infrared and visible image fusion framework (RSDFusion). A novel semantics-driven image fusion strategy is introduced in image fusion to maximize the retention of significant information of the source image in the fusion image. First, a semantically segmented image of the source image is obtained using a pre-trained semantic segmentation model. Second, masks of significant targets are obtained from the semantically segmented image, and these masks are used to separate the targets in the source and fusion images. Finally, the local semantic loss of the separation target is designed and combined with the overall structural similarity loss of the image to instruct the network to extract appropriate features to reconstruct the fusion image. Experimental results show that the RSDFusion proposed in this paper outperformed other comparative methods on both subjective and objective evaluation of public datasets and that the main target of the source image is better preserved in the fusion image.

Earth Observation data possess tremendous potential in understanding the dynamics of our planet. We propose the Semantics-driven Remote Sensing Scene Understanding (Sem-RSSU) framework for rendering comprehensive grounded spatio-contextual scene descriptions for enhanced situational awareness. To minimize the semantic gap for remote-sensing-scene understanding, the framework puts forward the transformation of scenes by using semantic-web technologies to Remote Sensing Scene Knowledge Graphs (RSS-KGs). The knowledge-graph representation of scenes has been formalized through the development of a Remote Sensing Scene Ontology (RSSO)—a core ontology for an inclusive remote-sensing-scene data product. The RSS-KGs are enriched both spatially and contextually, using a deductive reasoner, by mining for implicit spatio-contextual relationships between land-cover classes in the scenes. The Sem-RSSU, at its core, constitutes novel Ontology-driven Spatio-Contextual Triple Aggregation and realization algorithms to transform KGs to render grounded natural language scene descriptions. Considering the significance of scene understanding for informed decision-making from remote sensing scenes during a flood, we selected it as a test scenario, to demonstrate the utility of this framework. In that regard, a contextual domain knowledge encompassing Flood Scene Ontology (FSO) has been developed. Extensive experimental evaluations show promising results, further validating the efficacy of this framework.

Earth Observation data possess tremendous potential in understanding the dynamics of our planet. We propose the Semantics-driven Remote Sensing Scene Understanding (Sem-RSSU) framework for rendering comprehensive grounded spatio-contextual scene descriptions for enhanced situational awareness. To minimize the semantic gap for remote-sensing-scene understanding, the framework puts forward the transformation of scenes by using semantic-web technologies to Remote Sensing Scene Knowledge Graphs (RSS-KGs). The knowledge-graph representation of scenes has been formalized through the development of a Remote Sensing Scene Ontology (RSSO)—a core ontology for an inclusive remote-sensing-scene data product. The RSS-KGs are enriched both spatially and contextually, using a deductive reasoner, by mining for implicit spatio-contextual relationships between land-cover classes in the scenes. The Sem-RSSU, at its core, constitutes novel Ontology-driven Spatio-Contextual Triple Aggregation and realization algorithms to transform KGs to render grounded natural language scene descriptions. Considering the significance of scene understanding for informed decision-making from remote sensing scenes during a flood, we selected it as a test scenario, to demonstrate the utility of this framework. In that regard, a contextual domain knowledge encompassing Flood Scene Ontology (FSO) has been developed. Extensive experimental evaluations show promising results, further validating the efficacy of this framework. [ABSTRACT FROM AUTHOR]