Discovering Knowledge Hidden in Raster Images using RasterMiner

The satellite imagery data naturally exists as raster data. Useful information that can empower the domain experts to improve their decision-making abilities lies hidden in this data. However, finding this hidden knowledge is non-trivial and challenging due to the lack of open source integrated software to discover knowledge from raster data. In particular, existing open-source general-purpose data mining libraries, such as Knime [1], Mahout [3], Weka [5], Sci-kit [4], and SPMF [2], are inadequate to find knowledge hidden in raster datasets. In this talk, we present rasterMiner an integrated open-source software to discover knowledge from raster imagery datasets. It currently provides unsupervised learning techniques, such as pattern mining and clustering, to discover knowledge hidden in raster data. The key features of our software are as follows: (i) provides four pattern mining algorithms and four clustering algorithms to discover knowledge from raster data, (ii) Our software also provides "elbow method" to choose an appropriate k value for k-mean and k-means++ algorithms, (iii) Our software presents an integrated GUI that can facilitate the domain experts to choose algorithm(s) of their choice, (iv) Our software can also be accessed as a python-library, (v) The knowledge discovered by our software can be stored in standard formats so that the generated knowledge can be visualized using any GIS software.