The proposed Intelligent Geo-Search System (IGSS) is a knowledge aware, spectral oriented retrieval methodology. Knowledge about geographic objects and processes is formalized as an ontology. A reference spectral library is built consisting of spectral signatures. Tags are assigned to images using an endmember extraction algorithm and a labeling algorithm. Indexes such as (NDVI, NDMI, NBRI) and additional statistics for each index are stored along with the tags. In that way, queries can be formulated that enable both geographic entities detection (e.g. burned areas and forest type identification) and phenomena quantification (e.g. increased risk for forest fire), enabling more robust domain oriented question answering.
Technological advances in remote sensing increased the availability of satellite images with different spatiotemporal and spectral characteristics. The problem of obtaining data has been transformed into the difficulty of retrieving the most appropriate data for each user's needs. The biggest challenge is to bridge the gap between the low-level semantics (detectable, quantifiable features) of the images and the semantic information in them (Sethi et al., 2001) with the view to designing intelligent geo-search systems.
This bottleneck between low-level semantics of images and high-level semantics of user queries has already been acknowledged in the literature. Under the general term of content-based image retrieval a wealth of approaches has been introduced. We point the reader to Liu et al., (2007), Rani and Reddy, (2012) and Zhang et al., (2012) for a review and comparison of approaches via the prism of computer science.
Remote sensing images have special characteristics and therefore more specialized methodologies are needed (Xiran et al., 2012). Related work spans from the use of different data i.e. Veganzones et al., (2008) focus on techniques for hyperspectral remote sensing images while in the EU-funded project TELEIOS1, features are extracted from TerraSAR-X images and accompanied with image metadata and GIS data in order to unfold their semantics (Dumitru et al., 2011). The methodology in Maheshwary and Srivastava, (2009) is applied on multispectral images to different image processing and querying techniques.
Segment oriented techniques for relating low-level features of images and ontological concepts can be seen in Ruan et al., (2006), Li and Bretschneider, (2007), Liu et al., (2012) and Wang et al., (2013), while in Datcu et al., (2003), Li and Narayanan, (2004) and Aksoy et al., (2005) the labeling process is applied to pixels.
As far as the querying process is concerned, one research direction follows similarity metrics according to which a query image is compared to the image databased i.e. in Maheshwary and Srivastava, (2009) color and texture features are used for calculating the similarity between the query image and the images in the database. In contrast, in Ruan et al., (2006), Zhu et al., (2012) and Kaur and Jyoti, (2013) textual queries are posed to the system.
In the current work we designed a methodology for a semantically aware remote sensing image retrieval systems, targeted at expert users and certain type of satellite data. Having the apriori knowledge of the data constraints, the expertise of the users (i.e. that they won't try to search for a car in a Landsat image [900 m2 pixel area]), and the domain knowledge to be formalized, the complexity reduction, the increase of performance and the accuracy of results can be achieved. The main objective was to build a data and domain specific search system. Search was based on tags and spectral indexes, which composed the metadata of each image within the system.
The developed Intelligent Geo-Search System (IGSS) consists of 3 components: (a) knowledge formalization (ontology design and tag formulation), (b) Spectral Library (SL) building and (c) remote sensing image metadata formation. In the first phase, knowledge about the geographic entities, processes and indexes was formalized as an ontology. In the second phase, a spectral library was built consisting of the signatures of the objects contained within the ontology. The third step was the tagging of the remote sensing images using an endmember extraction algorithm (Nascimento and Dias, 2005) and a labeling algorithm (Sykas and Karathanassi, 2012). During this step indexes are calculated (NDVI, NDMI, NBRI). Statistics for each index (minimum and maximum values, mean, standard deviation and distribution type) are stored along with the tags.