Ant based supervised and unsupervised land use map generation from remotely sensed images

The land use or land cover map depicts the physical coverage of the Earth's terrestrial surface according to its use (viz. vegetation, habitation, water body, bare soil, artificial structures etc.). Land use map generation from remotely sensed images is one of the challenging task of remote sensing technology. In this article, motivated from group forming behaviour of real ants, we have proposed two novel ant based (one unsupervised and one supervised) algorithms to automatically generate land use map from multispectral remotely sensed images. Here supervised land use map generation is treated as classification task which requires some labeled pattern/pixel beforehand. Whereas the unsupervised land use map generation is treated as clustering based image segmentation problem in the multispectral space. Experimental results of the proposed algorithms are compared with corresponding popular state of the art techniques with various evaluation measures. Potentiality of the proposed algorithms are justified from the experimental outcome.