Land Cover Segmentation of Multispectral Images Using Multiresolution Algorithm

Abstract: Semantic segmentation is needed by regional planners to know the composition of land cover in their area, so that they can take the right policy. Several methods from manual to automatic have been researched, both based on colour and pattern. Each method has their strong and weakness, so it is necessary to make the right choice when applying the method. Currently, multispectral imagery is still very rarely used, even though sources of information from the internet are easy to find, i.e. Landsat imagery from the United States Geological Survey. This study uses two methods for segmenting three-channel multispectral images (red, green, and blue), namely iterative self-organizing clustering (ISOCLSUT), which is based on a colour sensor, and a multiresolution algorithm, which is based on colour and pattern. For the experiment, the pre-processed satellite image of Karawang district was segmented using the ISOCLUST as well as multiresolution algorithm. The experimental results show that land cover segmentation with multiresolution algorithm is better than ISOCLUST for RGB but for more than three channels, i.e., seven frequency channels, ISOCLUST shows better performance compared to real image conditions.

Keywords: Satellite Imagery, Multispectral, ISOCLUST, Multiresolution