Dimensionality Reduction for Visual Data Mining of Earth Observation Archives

Abstract

Modern knowledge discovery systems, empowered by visual data exploration techniques, enable the user to discover and understand the data content. Considering patch-level processing, the visual exploration of Earth Observation archives aims to identify groups of items sharing similar semantic content. Each patch is further represented by certain descriptors, i.e., spectral signatures or Weber local descriptors, to capture structural signature. Later on, the content of the archive is illustrated by a 3-D projection of the high-dimensional space of the descriptors. Aspiring to prove the visual data mining potential, this letter intends to determine the capability of dimensionality reduction techniques to achieve a meaningful 3-D projection of the high-dimensional space. Several real-world data sets were used, i.e., University of California, Merced Land Use data set and a Landsat 7 Enhanced Thematic Mapper Plus image tiled into patches.