Open AccessTASK-DEPENDENT BAND-SELECTION OF HYPERSPECTRAL IMAGES BY PROJECTION-BASED RANDOM FORESTS
Author(s) -
R. Hänsch,
O. Hellwich
Publication year - 2016
Publication title -
isprs annals of the photogrammetry, remote sensing and spatial information sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.356
H-Index - 38
eISSN - 2194-9042
pISSN - 2196-6346
DOI - 10.5194/isprsannals-iii-7-263-2016
Subject(s) - hyperspectral imaging , random forest , computer science , pattern recognition (psychology) , artificial intelligence , classifier (uml) , dimensionality reduction , land cover , spectral bands , feature selection , projection (relational algebra) , feature extraction , spectral signature , remote sensing , data mining , land use , geography , algorithm , civil engineering , engineering
The automatic classification of land cover types from hyperspectral images is a challenging problem due to (among others) the large amount of spectral bands and their high spatial and spectral correlation. The extraction of meaningful features, that enables a subsequent classifier to distinguish between different land cover classes, is often limited to a subset of all available data dimensions which is found by band selection techniques or other methods of dimensionality reduction. This work applies Projection-Based Random Forests to hyperspectral images, which not only overcome the need of an explicit feature extraction, but also provide mechanisms to automatically select spectral bands that contain original (i.e. non-redundant) as well as highly meaningful information for the given classification task. The proposed method is applied to four challenging hyperspectral datasets and it is shown that the effective number of spectral bands can be considerably limited without loosing too much of classification performance, e.g. a loss of 1 % accuracy if roughly 13 % of all available bands are used.