Increasing the classification efficiency of hyperspectral images due to multi-scale spatial processing

Classification of the land cover types from multi- and hyperspectral (HS) imagery is traditionally carried out on the basis of analysis of scatter plots of pixel values in a multidimensional feature space, which are used as brightness in individual channels. To increase the reliability of HS image classification, approaches are used based on simultaneously accounting for the characteristics of each pixel and the nearest-neighbor pixels, i.e., on the joint analysis of spectral and spatial features. The pixel neighborhood analysis is performed at various stages of the classification process.
In this work, using a test hyperspectral image, the efficiency of spectral-spatial data classification methods that take into account spatial information at various stages of processing is studied. Special attention is paid to selecting the size of the spatial processing core. It is shown that the best results are obtained by combining pre-processing of raw data before performing the procedures of pixel-by-pixel spectral classification and post-processing of the resulting maps. Prospects of multi-scale smoothing of initial images, with the increase of the number of spectral-spatial features being multiple of the number of the scales, are shown.