Visual Data Mining applied on Earth Observation datasets

In the quest of developing more accurate methodologies for Earth Observation (EO) image retrieval, visualization and information content exploration, a deep understanding of the data being analyzed is needed. In this paper we propose a simple but efficient visual data mining methodology that can be used for these tasks. Our solution consists in a patch-based feature extraction to derive image features and the projection of the achieved high dimensional feature space in a 3D space using dimensionality reduction methods. Gabor, Spectral Histogram and Bag-of-Words descriptors are the features assigned to represent the content of the data while PCA and t-SNE are the methods designed to achieve the 3D representation. The quality of information provided by the 3D visualization of the data depends on the extracted features. Therefore, a Sentinel-2 scene with various thematic classes is used for feature extraction and classification, to prove the performance of the selected descriptors