Although there has been successful work in developing image mining algorithms to extract in-formation, the visualisation of query results in Image Information Mining (IIM) systems is still not well adapted to the human cognitive skill of visual information processing. The main value of processing remotely sensed images is reflected in basic factors of visual atten-tion, i.e. the ability to promptly locate (where) and easily decode (what) geographic information for making inferences. The semantic component is reflected in the work of engineers in the field of Content-based Image Retrieval (CBIR). In addition, human visual attention is guided auto-matically by sensory stimulation affording users to know where relevant information is located. This work concentrates on the cognitively adequate visualisation of the location of relevant in-formation to support users to navigate rapidly towards information of interest. Supporting users in directing their attention towards the location of relevant information reduces the cognitive workload retained for processing context information and decision making. Therefore, we con-sider neurocognitive foundations of visual information processing and theories of relevance to frame appropriate design principles. Based on these principles, we establish a design methodol-ogy for attention guiding visualisation (AGV) and illustrate adapted visualisations. By applying a computed visual attention model, we evaluate proposed visualisations and relate results to up-coming research challenges for the effective visualisation in remote sensing IIM systems.     «

Although there has been successful work in developing image mining algorithms to extract in-formation, the visualisation of query results in Image Information Mining (IIM) systems is still not well adapted to the human cognitive skill of visual information processing. The main value of processing remotely sensed images is reflected in basic factors of visual atten-tion, i.e. the ability to promptly locate (where) and easily decode (what) geographic information for making inferences. The semantic...     »