How to find satellite images with a semantic concept?
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 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.
Content-based remote sensing image retrieval techniques have been introduced for bridging the gap between low-level semantics of images and high-level semantics of user queries.
The developed 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.
One important component of the system is its knowledge base. This, refers to the domain concepts about geographic objects that are present in remote sensing images. This knowledge is formalized in the IGSS ontology.
Metadata Assignment
The metadata assignment phase refers to the process in which meaningful information is attached to the low-level semantics of an RSI. Each RSI, that is contained in the IGSS, is enriched with information bridging the gap between ‘what is in an image’ (Camara et al., 2001) and high-level concepts in user queries. The information that is attached to each RSI includes the following: labels statistics per index for the whole image statistics per index for each label weights The “labels” correspond to concepts such as vegetation, water, soil, etc for each matched extracted endmember with the reference SL. The “statistics per index for the whole image” include the minimum, maximum, mean and standard deviation values from the NDVI, NDMI and NBRI for the whole image while the “statistics per index for each label” refer to the above mentioned values restricted to each label. The “weights” refer to the ratio between the number of pixels for each label divided by the total number of pixels of the image. For each label one “weight” is calculated.
Concluding
The methodology utilises the specific spatiotemporal, the spectral characteristics of the RSIs and the user’s expertise to formulate the ontology. A reference spectral library is built in order to label extracted endmembers and provide the appropriate metadata labels, based on the ontology’s entries. In addition the statistical characteristics of the indexes are used as metadata in order to formulate more complex user queries. An automatic methodology is developed for attaching these metadata to RSIs. The IGSS has two main milestones: i) domain oriented queries and ii) automatic metadata production for RSIs.
