**3.2 PCA**

Principal component analysis is a technique used to reduce the dimensionality of multivariate and multispectral datasets such as images with the aim of preserving as much of the relevant information as possible. PCA provides a method for the reduction of redundant information apparent in multi-dimensional databases. PCA represents any object with a much fewer information compared to the original image. Minimization of the correlation of multidimensional bands is performed by mathematically transforming the multi-band into another vector space with a new basis [17]. PCA was performed on the aerial photos in combination with LiDAR nDSM raster. The result is a single multiband raster, this means that the result of the LiDAR nDSM and aerial photos is a raster with 5 bands in a single raster dataset [18].
