Investigation of the impact of dimensionality reduction and feature selection on the classification of hyperspectral EnMAP data

Revisiting Existing Classification Approaches for Building Materials Based on Hyperspectral Data

“…This has also been demonstrated for the classification of hyperspectral data, e.g. in (Melgani and Bruzzone, 2004;Le Bris et al, 2014;Chehata et al, 2014;Keller et al, 2016;Keller et al, 2017).…”

“…Consequently, the classification approach has to deal with more or less relevant features as well as with redundant and possibly even irrelevant features. This is quite important, since an increase of the number of considered features over a certain threshold typically results in a decrease in classification accuracy, given a constant number of training examples (Melgani and Bruzzone, 2004;Keller et al, 2016). This effect is commonly referred to as the Hughes phenomenon (Hughes, 1968), and it can be addressed by using either dimensionality reduction or feature selection techniques.…”

“…Consequently, the degree of redundancy might be considerable for the considered hyperspectral data which, in turn, typically reveals a negative impact on the classification results which has for instance been demonstrated in (Melgani and Bruzzone, 2004;Keller et al, 2016). To address this issue, we involve standard approaches for dimensionality reduction and feature selection as recently used for the classification of simulated hyperspectral En-MAP data (Keller et al, 2017).…”

“…For feature selection, we use an approach that has recently been proven to work well for the analysis of relevant spectral bands in simulated hyperspectral EnMAP data (Keller et al, 2017) and is referred to as Correlation-based Feature Selection (CFS) (Hall, 1999). In general, CFS accounts for (1) the correlation between features and classes to identify relevant features and (2) the correlation among features to identify and discard redundant features.…”

Geospatial Computer Vision Based on Multi-Modal Data—How Valuable Is Shape Information for the Extraction of Semantic Information?

“…However, particularly for a more detailed scene analysis as for instance given by a fine-grained land cover and land use classification, multi-or hyperspectral data offer great potential. In this regard, hyperspectral information in particular has been in the focus of recent research on environmental mapping [28][29][30]. Such information can, for example, allow for distinguishing very different types of vegetation and to a certain degree also different materials, which can be helpful if the corresponding shape is similar.…”