Quantitative Evaluation of the Feature Space Transformation Methods Used for Applications of Visual Semantic Clustering of EO Images

Abstract

Data visualization guides the process of indexing and retrieval, strengthening the link between low-level image features and high-level human understanding of image content. In this regard, we have described the semantic content of a multidimensional dataset using its descriptors to derive high-dimensional feature spaces. The dimensionality of these spaces is further reduced to three in order to provide a three-dimensional (3-D) representation of the dataset items. Our main challenge was to identify the transformation that projects the high-dimensional feature set into a 3-D space preserving its semantic content. To overcome this issue, we have compared the efficiency of 11 feature space transformations: one feature selection algorithm and ten dimensionality reduction methods. As long as the dataset properties, during mapping, may differ depending on the chosen algorithm, the performance comparison of multiple algorithms is a difficult task. Therefore, three quantitative measures have been used: “Trustworthiness,” “Continuity,” and “ $Q_{NX}$ ”—the number of points preserved in data neighborhoods over projection. The mapping algorithms have been applied to three remote sensing datasets achieved from different sensors: LANDSAT 7 ETM+ and WorldView-3.